Essays on Dental Caries

#### **Chapter 2**

## Different Modalities in Caries Detection and Diagnosis

*Anfal Muhammad Alqussier*

#### **Abstract**

Dental caries is the most common chronic disease affecting humans worldwide. Early diagnosis of dental caries lesions allows more conservative treatment options to be followed. This may positively affect the prognosis of the condition and longevity of dental restorations. The optimum diagnosis approach should be used for better management of caries lesions. This chapter discusses several caries diagnostic modalities and systems, such as visual-tactile examination, dental radiography, transilluminationbased devices, electronic caries monitors, fluorescence-based devices, and others. Furthermore, different diagnostic approaches for detecting caries lesions around different dental restorations are reviewed at the end of this chapter. Proper selection and manipulation of diagnostic tools help to enhance the outcome of dental examination. Examination should be done in clean and dry teeth for proper examination.

**Keywords:** dental caries/diagnosis, sensitivity and specificity, lasers\*, humans, luminescent measurements/instrumentation\*, cone-beam computed tomography, dental caries/diagnostic imaging, radiography, bitewing, tomography, optical coherence, ultrasonography, transillumination, fluorescence, composite resin

#### **1. Introduction**

Dental caries is a complex, noncommunicable, dynamic disease caused by biofilms. Biological, behavioral, psychological, and environmental factors all have a role in its development [1]. It has a very high incidence and prevalence throughout the world [2, 3].

The lytic effect caused by bacterial (mostly *Streptococcus mutans*) metabolism byproduct on enamel and exposed dentin surfaces is considered the main etiological factor that is responsible for enamel/dentin demineralization and breakdown [1, 3, 4]. Fortunately, dental caries is considered the most preventable disease that can be treated noninvasively when detected early [5]. Caries activity is a term that describes the mineral balance through time as net mineral loss (demineralization), net mineral gain (remineralization), or stability. Caries inactive indicates caries arrest or regression; caries active indicates caries initiation or progression [1].

Caries diagnosis is the clinical judgment to evaluate the existence of the disease by integrating relevant information, including the detection and assessment of caries signs and symptoms. In contrast, caries detection is the identification of these signs

and symptoms. The primary purpose of caries diagnosis is to provide the greatest possible health outcome for the patient by enabling the selection of the most appropriate management and monitoring measures for the condition. This could be achieved by using valid and reliable detection tool/tools [1, 6]. Various caries diagnostic modalities and systems are discussed in this chapter, including visual-tactile examination, radiography, transillumination- and fluorescence-based devices, and others. Furthermore, at the end of this chapter, alternative diagnostic techniques for detecting secondary caries lesions are reviewed.

#### **2. Diagnostic tool in caries detection**

#### **2.1 Visual-tactile examination**

Although there are many different methods for caries detection, visual-tactile examination is considered the standard and the most used method in routine clinical examinations. It is usually combined with intraoral bitewing radiographs. The visual examination facilitated using a dental mirror and a ball-ended explorer should be performed gently on clean and dry teeth [5, 6]. International Caries Detection and Assessment System (ICDAS) and Nyvad Criteria are the most used scoring systems. They are used to clinically diagnose and evaluate caries based on a visual-tactile assessment [7].

Caries detection according to the visual-tactile examination method depends on the visual appearance and surface characteristic of the lesion [2, 5, 6]. Surface characteristics of the lesion may change according to the status of the caries activity. In the case of active caries lesions, the demineralization process progresses, and it is accompanied by rapid mineral loss. Active enamel caries typically appear whitish or yellowish with a loss of luster, and the texture feels soft when probed. It is frequently found in the pit and fissure, the gingival margin, and beneath the proximal contact points of both anterior and posterior teeth. They are usually covered with plaque. Moreover, active dentin lesions frequently appear brownish in color. The surface of the lesion feels soft, cheese-like, and fragile when probing [2].

On the other hand, when the demineralization process stops, the lesion is called arrested caries. The lesions' appearance is affected by the interruption of mineral loss and/or mineral regain (remineralization). The surfaces of arrested enamel lesions are often whitish or brownish in color. They are smooth and feel hard on probing. While arrested, dentin lesions frequently have dark brown/black surfaces that are hard and leathery on probing. Usually, arrested lesions are not covered with plaque [2].

Visual-tactile examination is thought to be conducted and interpreted rapidly with minimal invasion and little cost aside from professional training [5]. In addition to the excellent accuracy and diagnostic performance of the visual-tactile examination in detecting caries lesions, it allows better inspection of the interested field such as the detection of the presence of plaque accumulation which may affect the treatment planning [8]. However, relying solely on visual-tactile evaluation may lead to misdiagnoses and underestimation of early caries lesions. This occurs mostly on inaccessible surfaces, such as the proximal surfaces where adjacent teeth are present [5, 6]. To overcome its limitations, different diagnostic modalities should be allocated for caries detection in addition to visual-tactile examination during examination [8].

#### **2.2 Intraoral bitewing radiograph**

Intraoral radiographs are routinely used in conjunction with visual-tactile examination to detect caries in inaccessible areas. Bitewing radiograph is the most often utilized intraoral radiograph for evaluating inaccessible surfaces [8, 9]. It has been reported that radiograph examination is more sensitive than visual-tactile examination for detecting proximal and occlusal dentin lesions, determining lesion depth, and tracking lesion behavior [6]. The usage of intraoral digital radiography technology provides the benefit of quicker examination and image manipulation than the film radiograph technique. It allows the manipulation of image characteristics (such as contrast, brightness, sharpness, and other parameters) to improve images' clarity for better diagnosis and monitoring [5, 9].

Radiographic examination, on the other hand, has several limitations, such as exposure to ionization radiation. This might be a small but real risk, so a careful assessment of the patient's age, caries risk, and time since the last radiographs should be weighed [5]. Furthermore, radiography cannot differentiate between active and arrested lesions and occasionally between non-cavitated and cavitated lesions [6]. The performance of the examination depends on the skill and experience of the examiner, viewing conditions, and the type of the examined object [10].

In addition, intraoral radiograph radiographic examination, in conjunction with the visual-tactile examination, provides high specificity but low sensitivity in the detection of early caries lesions [5, 11]. An intraoral radiograph can detect caries lesions with only 30–60% demineralization, so it usually underestimates the extent of the caries lesions [12]. Radiographs are used in earlier diagnoses of proximal caries compared to the visual-tactile method. However, an occlusal lesion observed on a bitewing radiograph may have progressed to the middle third of the dentine. Therefore, it is no longer considered an early lesion that could be treated with remineralization techniques. This is explained by the anatomical noise caused by the complexity of superimposed crown structures on the two-dimensional images, making it harder to detect early occlusal lesions [8, 9, 12].

#### **2.3 Cone beam computed tomography (CBCT)**

Cone beam computed tomography (CBCT) is a modified type of medical computed tomography that uses a cone-beam of radiation rather than the conventional fan beam. The main advantage of the CBCT is that it provides three-dimensional (3-D) images that allow better observation and evaluation of target tissues. Furthermore, CBCT generates images in lesser radiation doses and at a lower cost than conventional medical computed tomography [11]. Several studies were conducted to evaluate the performance of CBCT in the detection of enamel and dentin caries lesions. They concluded that CBCT could be used as a valuable tool in proximal caries detection [12–14].

Compared to intraoral radiography, CBCT showed higher sensitivity in both enamel and dentin caries detection. Since the CBCT can detect caries at a lower rate of demineralization (**Figure 1**) [12–14], aside from its ease of use, the CBCT produces 3D images that are free of distortion and superimposition. Also, the images could be examined in different sections and planes, which could provide additional useful information [12].

Nonetheless, the CBCT is not widely available, which restricts its use in routine dental examinations [13]. Furthermore, because the CBCT emits more radiation than

#### **Figure 1.**

*Comparison of the diagnostic accuracy of CBCT and intraoral radiography for proximal caries detection. Schematic (the upper row), CBCT images (the middle row), and intraoral radiograph images (the lower row); (a) absence of proximal caries (score 0), (b) enamel caries (score 1), (c) caries extended to the outer half of dentin (score 2), and (d) caries extended to the inner half of dentin (score 3). Taken from: [12].*

intraoral radiography, it is not recommended for regular caries detection. It might, however, be utilized to detect caries lesions when CBCT is used for other purposes, such as preparing for dental implant placement [12, 13].

#### **2.4 Illumination-based devices**

Other approaches for detecting caries lesions include illumination-based devices. Three types of illumination-based devices use various ways of application and interpretation: optical coherence tomography (OCT), near-infrared (NIR), and fiber-optic technology (and more recently, digital fiber optics [FOTI/DIFOTI]). Each illumination-based approach employs a distinct wavelength [5]. In the following sections, a brief discussion of each type will be issued.

#### *2.4.1 Fiber-optic transillumination (FOTI) and digital imaging fiber-optic transillumination (DIFOTI)*

Fiber-optic transillumination (FOTI) is a simple noninvasive procedure that depends on illuminating the teeth – using a hand-held device – with a high-intensity narrow beam of white light. It is considered a valid and widely accepted method for proximal caries lesion detection. Based on the principle of FOTI, a dark shadow appears when the surface with disrupted enamel crystals – due to the demineralization process – is examined. This happens due to the changes in light scattering and absorption of light photons [5, 11].

#### *Different Modalities in Caries Detection and Diagnosis DOI: http://dx.doi.org/10.5772/intechopen.113363*

 Digital imaging fiber-optic transillumination (DIFOTI) is an improvement on traditional FOTI. It works on the same principle as the FOTI and employs visible light (450–700 nm) in conjunction with a camera equipped with a charge-coupled device (CCD) that may be connected to software. DIFOTI can capture real-time images of the occlusal, buccal, and lingual surfaces. Still, a subjective interpretation of the obtained images by an examiner is required. The obtained images could be used as a reference for further monitoring of the lesion [ 5 , 10 , 11 ].

 Fiber-optic transillumination (FOTI) is widely available in dental clinics and is easy to use. FOTI – in conjunction with visual-tactile examination – may enhance the detection of enamel proximal caries in the anterior teeth and dentin proximal caries in the posterior teeth [ 5 ]. Furthermore, DIFOTI has several advantages over bitewing radiography, including the elimination of the radiation risk associated with bitewing radiography technique, real-time image viewing, reduced patient discomfort due to the absence of intraoral films or sensors, and a higher sensitivity for early caries detection [ 11 ]. DIFOTI – in conjunction with visual-tactile examinations – is suitable for the detection of non-cavitated proximal caries. Its performance improves when the probe is placed on the buccal and lingual surfaces rather than on the occlusal surface alone [ 15 ]. An in-vitro study reported greater diagnostic accuracy of DIFOTI in the detection of proximal enamel caries in premolar teeth compared to conventional

 **Figure 2.**

 *Two sets of images representing enamel proximal caries using (a) DIFOTI, and (b) digital radiograph, and dentin proximal caries using (c) DIFOTI, and (b) digital radiograph. Taken from: [ 10 ].* 

film and digital radiographs. However, the diagnostic accuracies of the three methods in the detection of proximal dentin caries are comparable (**Figure 2**) [10].

Digital imaging fiber-optic transillumination (DIFOTI) has not been shown to objectively quantify lesion size, depth, volume, and mineral content. DIFOTI is unable to distinguish between carious lesions and developmental defects such as fluorosis. Thus, it may give high false-positive values, potentially leading to overtreatment. Furthermore, it does not determine the status of caries activity. More in-vitro and clinical studies are required to ensure and enhance the performance, diagnostic accuracy, and reliability of FOTI/DIFOTI [5, 11].

#### *2.4.2 Near-infrared transillumination (NIRT) and near-infrared reflection (NIRR)*

Near-infrared transillumination (NIRT) devices are devices that were first introduced in 2012. They use the same principle of transillumination as FOTI and DIFOTI. However, instead of visible light, these devices illuminate the tooth with nearinfrared (wavelength: 780–850 nm) light with deeper penetration through the tooth structure [5, 16, 17]. The system involves a CCD sensor to obtain the images, computer connection, software, and elastic arms containing the light source. The emitted near-infrared (NIR) light can be transmitted through the gingiva, alveolar bone, tooth root, and crown. The obtained image is revealed from the occlusal surface. Like the DIFOTI, the obtained images need interpretation by an examiner [5, 16].

NIRT devices are widely available in dental clinics and are easy to use. Examples of commercially available devices include DIAGNOcam (KaVo, Biberach, Germany) and the recently introduced iTero Element 5D (Align Technologies, San Jose USA) and TRIOS 4 (3Shape, Copenhagen, Denmark) with two intraoral scanning tools [5].

Near infra-red transillumination (NIRT) device is a noninvasive tool that permits the diagnosis of non-cavitated proximal caries lesions without the risk of ionizing radiation [17]. It showed higher sensitivity than a bitewing radiograph in detecting early enamel lesions [18]. Moreover, it has been reported that NIRT showed comparable performance in detecting lesions involving the dentin-enamel junction (DEJ) to bitewing radiographs and visual-tactile examinations [18, 19].

Even though NIRT revealed a good diagnostic performance in detecting occlusal caries lesions and identifying sound teeth, it tends to overestimate [19]. In addition, NIR has low sensitivity in the detection of early enamel caries lesions [5].

Another near-infrared-based technology is the use of near-infrared reflection (NIRR) in the diagnosis of caries lesions. Both NIRT and NIRR illuminate the tooth using a near-infrared light that is scattered by carious enamel. In the NIRR method, the scattered light results in a strong reflection of the light on the sensor. Accordingly, an increase in local light intensity at a carious lesion compared to the adjacent intact tissues makes caries appear brighter. However, caries lesions are seen in the NIRT method as dark shadows due to the scattering of light within the dentin. This makes the dentin act as a homogeneous light source that illuminates the whole enamel and dentin surfaces except for the caries lesions, which appear as dark shadows [17, 20].

It has been reported that NIRR showed comparable diagnostic performance to bitewing radiograph in the detection of enamel caries lesions. However, NIRR showed some limitations. It showed low sensitivity for proximal caries detection (**Figure 3**) [17, 20, 21]. In addition, teeth with opaque enamel – especially molars – makes the detection of non-cavitated proximal caries lesion impossible with NIRR [17]. It could not be used solely in the diagnosis of proximal caries lesions. Yet, more studies are still

*Different Modalities in Caries Detection and Diagnosis DOI: http://dx.doi.org/10.5772/intechopen.113363*

 **Figure 3.**

 *Premolar with non-cavitated proximal lesion that was not detectable in clinical occlusal (a) and lingual views (c). Using NIRR, a white spot was visible in the occlusal view (b) but not in the lingual view (d). Taken from: [ 17 ].* 

required to evaluate and enhance the efficacy of NIRT and NIRR devices in measuring the exact lesions' depth, reliability, and validity, especially in the detection of proximal caries lesions.

#### *2.4.3 Optical coherence tomography (OCT)*

 Optical coherence tomography (OCT) is an interferometric technique that establishes cross-sectional images of biological structures without the negative effect of ionization radiation exposure. It uses coherent light with a near-infrared wavelength that has maximum depth of penetration through the biological tissues [ 5 , 7 , 22 ]. The first use of OCT in dental research was done by Colston et al. in 1998 [ 23 ]. In dentistry, OCT is used for many applications such as caries detection, evaluation of marginal integrity of tooth restoration, and tooth crack diagnosis [ 11 ].

 Optical coherence tomography (OCT) is a noninvasive tool that creates real-time 3D images at micrometer resolution through light reflection and backscattering based on the optical absorption and scattering properties of the examined tissue [ 7 , 11 ]. The OCT imaging depth is significantly impacted by the medium's translucency. Structures that do not transmit light and deeper structures are irrelevant for OCT imaging. Sound enamel is practically transparent at the OCT wavelength range. The

dentin-enamel junction (DEJ), which appears as a dark border, helps to distinguish between enamel and dentin in the OCT images. The caries tissues are shown as bright areas due to the development of multiple micro-porosities where the OCT signal's backscatter increases [ 22 ].

 Optical coherence tomography (OCT) is an industry-ready technology that is relatively easy to use and can be applied with low optical power [ 24 ]. Compared to NIRT and FOTI devices, OCT showed superior sensitivity and better performance during caries detection. It could be used as a complementary tool with conventional clinical examination methods for clearer diagnosis [ 5 , 7 ].

 Swept-source optical coherence tomography (SS-OCT) is a modification type of conventional OCT systems [ 11 , 22 ]. SS-OCT utilizes an interferometer with a narrow linewidth, frequency-sweep laser, and detectors to determine interference versus time. The latest SS-OCT devices provide real-time cross-sectional images with microscopic-level resolution ( **Figure 4** ) [ 22 ]. SS-OCT showed higher sensitivity and specificity than bitewing radiograph in caries detection at enamel and outer one-third dentin. However, for deep caries, SS-OCT showed lower sensitivity than bitewing radiograph but with similar specificity for both methods. This was explained by the greater light scattering in dentin than in enamel [ 25 ].

 On the other hand, the OCT's primary flaw is the significant scattering of light at near-infrared wavelengths. Depending on the structure, this restricts the penetration depth from a few tens to hundreds of microns [ 24 ]. Furthermore, in the OCT images, the pulp chamber may not be clearly shown, thus preventing the determination of

#### **Figure 4.**

 *SS-OCT image of smooth surface enamel caries as bright zone (white arrows) in (A1, and B1), and its corresponding histological view after cross-sectioning (A2, and B2). A non-cavitated enamel lesion is seen in (A1 and A2). However, (B1 and B2) shows a cavitated enamel lesion. Taken from: [ 22 ].* 

exact lesion extension concerning the pulp [25]. OCT is a noninvasive and safe method for the diagnosis of dental caries and is suitable for use in both pregnant women and small children [22]. The limited utilization of OCT devices in dental clinics is a result of their limited availability compared to OFTI and NIRT devices [5]. More studies are needed to evaluate the caries diagnostic accuracy of OCT and SS-OCT.

#### **2.5 Electronic caries monitor (ECM)**

The demineralization reaction has been shown to impact the electrical conductivity of the tooth. The bulk resistance of dental tissue is measured using the ECM device. Higher conductivity has been documented in caries teeth due to increased porosity within caries tooth structure and the presence of saliva within these pores. These result in a reduction of electrical resistance [5].

Electronic caries monitor (ECM) measures the electrical bulk resistance of dental tissue using a single, fixed-frequency alternating current. Both enamel and exposed dentin surfaces can be measured. A probe is used to send electricity through the tooth and body to a counter-electrode, which is typically kept in the patient's hand. Because the body has low resistance in comparison to dental tissues, the resistance value typically closely represents that of the tooth near the probe contact point (**Figure 5**) [26]. It has been reported that ECM is a sensitive, practical caries diagnostic tool and has been widely used in clinical studies. Utilizing ECM offers an advantage over the sole visual-tactile examination in the diagnosis of initial caries lesions diagnosis. Therefore, it has been suggested to use it in conjunction with the visual-tactile examination during dental examination [5, 26].

Although ECM is claimed to be more effective than FOTI and intraoral radiography in detecting early caries lesions, it has some limitations. As a confounding factor, the presence of stains on the investigated surfaces has been shown to affect the outcome of the ECM examination. Furthermore, the reproducibility of ECM is questionable due to the possibility of probe contact site inconsistency [5]. As a result,

**Figure 5.** *Electrical caries monitor. Taken from: [26].*

employing ECM to identify caries lesions should be done with caution and in combination with other caries detection methods.

#### **2.6 Fluorescence-based devices**

Demineralization of the enamel tissue results in modification of the structure's features and physical properties. Thus, If the demineralized structure is exposed to fluorescent light, it gives a different response than healthy structures. This may help in the detection and diagnosis of caries lesions. Fluorescence-based devices are classified into two categories based on the type of type of emitted light, i.e., light fluorescence, and laser fluorescence [5]. In the following sections, a brief review of each type will be discussed.

#### *2.6.1 Light fluorescence*

The qualitative light-induced fluorescence (QLF) technique can quantify small alterations in teeth based on autofluorescence, which occurs when the tooth is exposed to 405 nm visible blue light. The QLF device is composed of a light-emitted diode (LED), an inductor filter, and a metal oxide semiconductor sensor [27].

Fluorescence loss occurs because of tooth demineralization. The greater the mineral loss, the greater the loss of tooth fluorescence. Even early lesions with minor mineral changes can be detected and monitored effectively [6, 27]. When a lesion exists, an increase in light scattering causes the lesion to appear as dark patches on a bright green background. The fluorescent images of the tooth are digitalized and quantitatively assessed concerning the adjacent healthy tooth structures. A lesion is defined as any region with a reduction in fluorescence of more than 5% [6].

Qualitative light-induced fluorescence (QLF) is a noninvasive that can be used – in conjunction with the conventional visual-tactile and radiographical examination- to enhance the diagnosis of early enamel caries. It aids in the detection, quantification, and monitoring of early caries lesions that conventional methods may miss [6, 27]. Furthermore, QLF may determine both the depth and the bacterial activity of dental caries at the same time. QLF also avoids the negative consequences of radiation exposure that are linked with traditional radiographic evaluation [28].

QLF shows excellent performance and sensitivity in the detection and quantification of early smooth and occlusal caries lesions (**Figure 6**) [6]. In addition, Oh et al. reported a higher performance of QLF in the detection of occlusal caries lesions than the conventional methods alone but not in the detection of proximal caries. Because the intensity of light transmitted through the occlusal surface is already reflected before reaching the lesion in proximal caries, identification of the lesion is difficult if the degree of caries does not exceed a particular threshold [29]. The performance of the QLF evaluation may be affected by the presence of confounding factors such as the presence of dental plaque, staining, debris, and saliva [6, 29, 30]. Therefore, a clinician should not rely solely on QLF in the detection of caries lesions.

#### *2.6.2 Laser fluorescence*

Laser fluorescence (LF) is a noninvasive device used to detect caries lesions and estimate their depth by exposing the tooth to a non-ionizing laser [31]. It consists of a tip that emits monochromatic red light at 655 nm wavelength and a sensor to detect the backscattered fluorescence from the examined tooth and produce a two-dimensional hyperspectral image (**Figure 7**) [6, 31].

*Different Modalities in Caries Detection and Diagnosis DOI: http://dx.doi.org/10.5772/intechopen.113363*

 **Figure 6.**  *Smooth surface caries detection using QLF technology. Taken from: [ 6 ].* 

 **Figure 7.**  *Laser fluorescence caries detector (DIAGNOdent TM Pen, KaVo). Taken from: https://www.kavo.com .* 

 Carious teeth generate fluorescence proportional to the degree of caries, whereas clean and healthy teeth produce no or little fluorescence. It was proposed that these fluorescence changes are caused by protoporphyrin, a photosensitive pigment found in carious tissues as a consequence of bacterial metabolic activity [ 11 ].

 Laser fluorescence (LF) has been found to tend to higher specificity than sensitivity for enamel caries detection. The performance of the LF is better with larger lesions [ 6 ]. In addition, Kapor et al. concluded that LF examination shows high sensitivity and specificity. However, it should not be used alone to avoid overtreatment [ 8 ]. In comparison to QLF, Diniz et al. reported higher sensitivity of laser fluorescence device (LF pen) in the detection of occlusal caries [ 32 ].

Several confounding factors, including the amount of plaque, calculus, and/or discoloration on the tooth surface, as well as the degree of dehydration of dental tissue, may influence the LF evaluation [6]. Thus, LF may be used as an adjunct examination method to enhance the detection of early caries lesions that could not be detected properly using visual-tactile examination alone.

#### **2.7 Frequency-domain infrared photothermal radiometry and modulated luminescence (PTR/LUM)**

Photothermal radiometry and modulated luminescence PTR/LUM technology are based on the detection of optical and thermal changes in the tooth structure [33]. PTR is based on the modulated thermal infrared response (also known as a black body or plank radiation) of a medium because of repeatedly exposing a specimen to radiation. Black body radiation is the electromagnetic energy emitted by a black body when its temperature is constant and uniform, or from a body that is in thermodynamic equilibrium with its surroundings. The sample surface's temperature changes because of the absorbed radiation energy being transformed into thermal energy. A PTR signal-generating infrared detector can be used to measure the change in thermal emissions due to temperature modulation. LUM depends on the transformation of optical energy into radiation energy. When optical energy from a laser source is absorbed by a molecule, it causes excitement to its chromophores into a higher energy status. Then, after de-excitation to lower energy status, longer wavelength energy will be emitted. This could be detected by a photodetector that generates a LUM signal [34].

Canary caries detection system is based on PTR/LUM technology [11]. It was developed by Quantum Dental Technologies (Toronto, ON, Canada) in 2009 and used in a Health Canada-approved human investigational trial [34]. According to the manufacturer, the Canary caries detection system (**Figure 8**) can detect caries from 50 to 5 mm depth including secondary caries detection around sealant and restorations' margins. Canary caries detection system values are not affected by stains and the presence of calculus or saliva [11].

#### **Figure 8.**

*The canary system from quantum dental technologies. Taken from: https://www.dentalcompare.com.*

*Different Modalities in Caries Detection and Diagnosis DOI: http://dx.doi.org/10.5772/intechopen.113363*

An in-vitro study compared the performance of the Canary caries detection system in the detection of proximal caries lesions to the performance of visual-tactile and radiographical examinations. They used polarized light microscopy as a gold standard in caries detection. A higher sensitivity of the PTR/LUM caries detection system compared to the other two examinations tested was reported. There was no significant difference in specificity between the radiographical examination and the PTR/LUM method. In contrast, the specificity of the visual-tactile examination was the lowest among the three examination methods [33]. In addition, a study by Xing et al., in 2023, reported a positive moderate correlation with the caries depth of the PTR/LUM system; even a 20° deviation from perpendicular did not affect its performance [35]. Also, it has been reported that the PTR/LUM system can detect non-cavitied proximal lesions without the use of ionization radiation [36]. However, the PTR/LUM caries detection method is still considered new, and more in-vitro and clinical studies are required to evaluate its performance and accuracy.

#### **2.8 Ultrasound**

The first use of ultrasound in dentistry was by Baum et al. in 1963 [37]. Sound waves with frequencies (20 kHz) higher than those heard by humans are used in ultrasound technology. During caries detection, ultrasound depends on the substantial variations in sonic conductivity between sound and demineralized dental structures. The use of ultrasound to detect caries is considered simple, safe, and provides real-time images, among other benefits [11]. A recent study reported a high correlation between histological and ultrasound examinations' results in the detection of smooth surface enamel caries [38].

Although it showed promising performance in caries detection, there are only limited studies done to evaluate the efficacy of the ultrasound system for caries detection.

#### **3. Detection of secondary caries lesions**

Secondary caries (also known as recurrent caries) are caries lesions that form near the margin of a restoration. Other examination approaches besides the visual-tactile assessment could improve the longevity of the restoration. Secondary caries could be predicted by the presence of marginal ditching, discoloration of adjacent tooth structures, and gaps at the tooth-restoration interface. Unfortunately, these signs are not reliable predictors making the detection of secondary caries lesions with solely visual examination challenging. Alternatively, relying solely on radiographic evaluation may result in an underestimate of lesion extent. The radiopacity of restorative materials may contribute to secondary caries misdiagnosis [39].

Several studies suggested the use of LF along with visual-tactile and radiographical examinations for the detection of secondary lesions [40, 41]. While NIR transillumination appears to be promising in the detection of secondary caries, there is little data to support it. Even though just a few studies have been conducted to evaluate the use of visual-tactile evaluation with QLF, this method has demonstrated poor performance [40].

While CBCT images detect proximal caries more correctly than bitewing radiography, the presence of high-density restorative materials such as dental amalgam and porcelain restorations results in metal artifacts. This lowers the CBCT's

#### **Figure 9.**

 *SS-OCT is not able to detect caries lesion underneath the metal alloy in the first molar. SS-OCT image of proximal contacts between second premolar and first molar (scanning from occlusal surface). A distinct white line indicates the presence of caries (arrow). Cross-sectional imaging of the first molar is not possible due to the metal inlay (right). The presence of dentin caries in the mesial and distal proximal surfaces of the second premolar is confirmed after cavity preparation (left). Taken from: [ 22 ].* 

accuracy in detecting secondary caries lesions. Because of its atomic number and density, amalgam restoration produces more artifacts than porcelain and metalceramic restoration. As a result, there are more false-positive results [ 12 ]. However, other studies showed higher diagnostic accuracy of CBCT compared to bitewing radiography in the detection of secondary caries around resin composite restorations [ 14 , 42 ].

 Optical coherence tomography (OCT) has also been investigated for the detection of secondary caries. The presence of opaque and metallic restoration makes subsequent caries detection with OCT challenging ( **Figure 9** ). However, the satisfactory performance of the OCT in detecting secondary caries around tooth-colored restorations that can transmit light has been documented [ 22 ]. In addition, the detection of secondary caries around tooth-colored restorations could be established by the PTR/ LUM system. Abrams et al. reported that the PTR/LUM system (The Canary System, CS) has the potential for more accurate diagnosis of secondary caries around compomer and resin-modified glass ionomer restorations than visual-tactile examination, light-emitting diodes fluorescence, and LF used [ 43 ].

 Based on the exceeding, adjunctive diagnostic aids – in addition to conventional visual-tactile and radiographic examinations – should be used when detecting secondary caries lesions to improve diagnostic outcomes and avoid overtreatment. This is particularly applied when examining teeth with high-opacity restorations. Restorations' finishing may be required to overcome the limits of some diagnostic aids that are influenced by the presence of staining.

#### **4. Conclusion**

 In conclusion, the most common and widely used approach for caries detection and assessment is a visual-tactile examination combined with a radiographic examination. It does, however, have certain limits. As a result, various diagnostic tools and modalities are available and might be employed to improve caries diagnosis accuracy and sensitivity. A proper caries detection method should be carefully selected and implemented on clean and dry surfaces. More laboratory and clinical research is required to assess and improve the performance of such methods.

### **Conflict of interest**

The author declares no conflict of interest.

### **Author details**

Anfal Muhammad Alqussier Restorative Dental Science Department, College of Dentistry, Taibah University, Al-Madinah al-Munawwarah, Saudi Arabia

\*Address all correspondence to: anfal.m.q@gmail.com

© 2023 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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[8] Kapor S, Rankovic MJ, Khazaei Y, Crispin A, Schüler I, Krause F, et al. Systematic review and meta-analysis of diagnostic methods for occlusal surface caries. Clinical Oral Investigations. 2021;**25**(8):4801-4815. DOI: 10.1007/ S00784-021-04024-1

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[30] Amaechi BT, Higham SM. Quantitative light-induced fluorescence: A potential tool for general dental assessment. Journal of Biomedical Optics. 2002;**7**(1):7. DOI: 10.1117/1.1427044

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[36] Xing H, Eckert GJ, Ando M. Detection ability and direction effect of photothermal-radiometry and modulated-luminescence for noncavitated approximal caries. Journal of Dentistry. 2019;**90**:103221. DOI: 10.1016/j.jdent.2019.103221

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#### **Chapter 3**

## Revolutionizing Dental Caries Diagnosis through Artificial Intelligence

*Sukumaran Anil, Krishnaa Sudeep, Sudeep Saratchandran and Vishnupriya K. Sweety*

#### **Abstract**

The diagnosis and management of dental caries, a prevalent global oral health issue, have traditionally depended on clinical examination and the interpretation of radiographic images. However, with the rapid advancements in technology, the landscape of dental diagnostics is transforming. This chapter delves into the revolutionary impact of artificial intelligence (AI) on detecting and managing dental caries. Dental professionals can now achieve enhanced diagnostic accuracy by harnessing the power of machine learning algorithms and image recognition technologies, even identifying early-stage caries that conventional methods might overlook. The integration of AI into dentistry not only promises improved patient outcomes by facilitating timely interventions and streamlining clinical workflows, potentially redefining the future of oral healthcare. While the prospects are promising, it is imperative to concurrently address the challenges and ethical considerations accompanying AI-driven diagnostics to ensure that the technology augments, rather than supplants, the expertise of dental professionals. The chapter serves as a comprehensive overview of the current state of AI in dental caries diagnosis, its potential benefits, and the road ahead.

**Keywords:** dental caries, artificial intelligence, machine learning, early diagnosis, clinical decision-making, diagnostic imaging, image interpretation

#### **1. Introduction**

Dental caries is a pervasive global oral health issue, impacting individuals across all age demographics. Recognized by the World Health Organization as a significant public health challenge, it carries a substantial health burden due to its ubiquity and potential for severe complications [1]. This chronic condition is marked by the gradual degradation of dental hard tissues, primarily due to the metabolic activities of oral bacteria [2]. Such deterioration stems from the demineralization of tooth enamel and dentine, a result of acids produced by bacteria in plaque-a sticky film that continually forms on teeth. The etiological factors underpinning dental caries are diverse, encompassing microbial presence, dietary habits, host reactions, and temporal elements [2]. While initial manifestations might be subtle lesions on the enamel,

unchecked progression can lead to pronounced cavitations, threatening the tooth's structural integrity and paving the way for more grave dental issues, including pain, infection, tooth loss, and even broader systemic health complications [3].

Technology has been a driving force in improving oral healthcare, especially in recent decades. Artificial Intelligence (AI) stands out among the technologies making a transformative impact. At its core, AI is a branch of computer science that aims to create machines that mimic human intelligence [4]. This includes learning from experience, understanding language, recognizing patterns, solving problems, and making decisions. AI has many applications, from autonomous vehicles and voice assistants to healthcare diagnostics and treatment planning. AI has shown promising potential in healthcare, enhancing precision, improving patient outcomes, and streamlining processes. It has been applied in various areas, such as radiology, cardiology, oncology, and, more recently, dentistry. In the context of dental care, AI can offer several advantages. For instance, it can automate routine tasks, assist in the interpretation of radiographs, aid in diagnosing oral conditions, and even predict the risk of future oral health problems. This has profound implications for managing dental caries, where early and accurate detection is paramount. The use of AI in dentistry is an evolving field, with advancements in Machine Learning (ML), a subset of AI that uses statistical methods to enable machines to improve with experience, and image recognition playing a significant role [5]. These technologies have enabled the development of systems that can analyze dental radiographs or intraoral images, detect signs of dental caries, and alert the dentist to potential areas of concern.

However, as with any technological innovation, challenges and ethical considerations must be considered. Integrating AI into clinical practice requires understanding these aspects and strategies to manage them [6]. Despite these challenges, the potential of AI to revolutionize dental care is considerable. The promise of AI for the future of dental caries management is exciting. It can enhance accuracy in diagnosis, optimize treatment planning, improve patient outcomes, and advance dental care delivery. This chapter aims to provide a comprehensive understanding of the role of AI in dental caries, highlighting its potential, exploring its applications, discussing challenges and ethical considerations, and speculating about future directions.

#### **2. Methods for diagnosing dental caries**

Dental caries is one of the most widespread chronic diseases globally, affecting individuals across the lifespan. Effective management of dental caries hinges on early detection and accurate diagnosis [7]. Over time, several diagnostic methods have been developed and employed in dental practice, each with merits and limitations. The main techniques for diagnosing dental caries are visual-tactile examination, radiographic evaluation, and advanced imaging technologies [8].

#### **2.1 Visual-tactile examination**

The visual-tactile examination is the most basic and commonly used method for caries diagnosis. It involves thoroughly examining the teeth using a dental explorer and mirror, accompanied by sufficient lighting. The dentist assesses the teeth' color, texture, and transparency, looking for signs of decay such as discolorations, cavitations, or changes in enamel reflection. While this method is simple and cost-effective, it has limitations [8]. Early dental caries may not cause noticeable changes in the tooth's appearance, making visual-tactile examinations less effective for detecting caries in their initial stages.

#### **2.2 Photographs**

Intraoral photographs serve as valuable aids in diagnosing dental caries. Highquality digital images allow for visualizing teeth in detail, which can help identify early signs of caries. Photographs also enable longitudinal assessment of a patient's oral health, allowing for comparison over time to detect changes or progressions in dental caries [9]. However, while photographs can provide detailed surface images, they cannot provide insights into subsurface structures or the extent of internal decay.

#### **2.3 Radiographic examination**

Radiographic examinations, particularly dental bitewing X-rays, are widely used to diagnose dental caries [10]. They offer the ability to visualize both the surface and subsurface structures of teeth, enabling the detection of caries that might be hidden from the naked eye or in areas like interproximal spaces, which are hard to inspect visually. There are two primary types of dental X-rays: bitewings and periapical. Bitewing X-rays are beneficial for detecting caries in the crowns of teeth, especially between the teeth. In contrast, periapical X-rays can show the entire tooth, including the roots, and help detect abscesses and cysts [11]. However, radiation exposure, albeit minimal, and the potential for overdiagnosis are considerations with radiographic examinations.

#### **2.4 Advanced imaging techniques**

In recent years, advanced imaging techniques have emerged that aim to overcome the limitations of traditional methods. These include optical coherence tomography (OCT), laser fluorescence devices (DIAGNOdent), and near-infrared light transillumination [12]. For example, OCT can provide detailed, cross-sectional images of dental tissues, allowing for the early detection of caries. Laser fluorescence devices measure changes in tooth fluorescence to identify carious lesions. Near-infrared light transillumination exploits the different light-scattering properties of healthy and carious dental tissues to detect decay. While promising, these techniques have yet to be widely used in routine dental practice, primarily due to their cost and the need for specialized equipment and training.

Diagnosing dental caries involves a combination of methods, from the traditional visual-tactile examination to advanced imaging techniques. Each method has strengths and limitations, and the choice often depends on the clinical scenario, the dentist's expertise, and available resources [13]. As research progresses and technology advances, we can anticipate more precise and effective tools for caries detection, leading to improved patient outcomes.

### **3. The role of AI in dental caries diagnosis**

The integration of Artificial Intelligence in healthcare has opened new avenues for enhancing patient care, and dentistry is no exception. The use of AI in dentistry has been a transformative step, promising more accurate diagnoses, more efficient practices, and better patient outcomes. The use of AI in dentistry is only partially a recent development [14]. The journey began in the late 20th century with expert systems that attempted to replicate the decision-making abilities of

dental experts. For instance, the DENTSYS system developed in the 1980s used AI to generate dental treatment plans. However, in the last decade, AI's application in dentistry has gained significant momentum, spurred by advancements in machine learning algorithms and increased computing power. Researchers and tech companies developed AI tools to analyze dental images and detect oral conditions, including dental caries [15].

AI's importance in managing dental caries cannot be overstated. Dental caries is a prevalent condition, often requiring time-intensive analysis of dental images for diagnosis. AI systems, particularly those using machine learning, can quickly analyze these images and detect signs of caries. They save time and can improve the accuracy of diagnosis by catching early-stage caries that the human eye may miss [16]. AI can also be used to predict the risk of dental caries. By analyzing patient data and identifying risk factors, AI systems can predict a patient's likelihood of developing caries in the future. This allows for early intervention and preventive care, which is more cost-effective and beneficial for the patient's oral health [17].

The pressing need for early detection and intervention is evident. With the advent of digital dentistry, the incorporation of Artificial Intelligence (AI) heralds a transformative approach in the detection, management, and prophylaxis of dental caries, highlighting the significance of this condition both clinically and in the broader public health context. Artificial Intelligence (AI), an area that seeks to emulate human cognitive functions through machines, has witnessed rapid advancements over the past decades and has infiltrated various domains of healthcare, revolutionizing diagnostic and therapeutic processes [18]. Its integration within dentistry, particularly in dental caries detection and management, has shown promising potential. This chapter delves into AI's role in dental caries, offering insights into its applications, benefits, challenges, and prospects. But before we proceed to understand AI's role, it is pivotal to have a foundational understanding of dental caries. This condition remains one of the most prevalent oral diseases globally.

#### **4. AI methods used for diagnosis of dental caries from photographs and X-rays**

#### **4.1 Reinforcement learning**

This type of machine learning, where an agent learns by taking actions in an environment to optimize a reward, has seen less prominence in medical imaging than in supervised or unsupervised methods. Nevertheless, it holds substantial promise for specific applications. In dental imaging, reinforcement learning can serve vital roles, such as guiding image segmentation or pinpointing optimal imaging parameters. Ren et al. (2021) highlighted that this approach's potential in dental diagnostics is considerable [19].

#### **4.2 Generative adversarial networks (GANs)**

Generative Adversarial Networks, commonly known as GANs, are at the forefront of unsupervised machine learning algorithms, characterized by their unique structure of two contesting neural networks. These networks, working in tandem, have historically been deployed to craft synthetic images that closely replicate real ones. However, their application is not confined to this realm alone. Increasingly, GANs are finding

*Revolutionizing Dental Caries Diagnosis through Artificial Intelligence DOI: http://dx.doi.org/10.5772/intechopen.112979*

utility in augmenting data sets, creating additional synthetic training examples, and, crucially, anomaly detection. In dentistry, for instance, they can effectively identify teeth conditions that deviate from the expected, such as dental caries [20].

#### **4.3 Support vector machines (SVMs)**

SVMs, are specialized supervised learning models tailored for classification and regression tasks. In dental imaging, SVMs play an instrumental role in classifying images, particularly in determining the presence or absence of caries [21]. The distinctive feature of SVMs lies in their ability to generate a boundary, a demarcation that effectively separates various image classes. This separation is not arbitrary; the algorithm constructs it to ensure the maximal margin between the differing image categories [22].

#### **4.4 Random forests**

Random Forests are ensemble machine-learning algorithms that build upon multiple decision trees. The output is derived from the class mode of the results produced by these individual trees. As an ensemble learning method, it amalgamates the predictions from various machine learning models to yield a more precise prediction [23]. As corroborated by Breiman, when applied to dental health, Random Forests are particularly adept at classifying dental images and even forecasting the progression of diseases such as dental caries [24].

#### **4.5 Semantic segmentation**

Semantic Segmentation is a pivotal technique in computer vision that classifies individual pixels within an image into distinct categories. Applied to dental radiographs, this method can deftly categorize pixels into labels such as 'healthy tissue,' 'caries,' or 'other.' Such granular, pixel-level analysis furnishes a comprehensive assessment of dental images, facilitating the pinpointing of the exact location and extent of dental caries, thus aiding in accurate diagnostics [25].

#### **4.6 Self-supervised learning**

Self-Supervised Learning is a nuanced approach in machine learning, distinguished by its reliance on data's inherent structure for guidance. Abandoning the conventional dependence on explicit labels, these sophisticated algorithms derive pseudo-labels from the data to discern representations. Within the realm of dental caries detection, this methodology emerges as particularly advantageous [26]. By harnessing the power of extensive unlabeled dental image datasets, self-supervised learning can amplify a model's adeptness at extracting salient features. These enhanced models, equipped to predict sequences or deduce image orientations, set the stage for monumental advancements in diagnostic precision [27].

#### **4.7 Capsule networks**

Capsule Networks, often called CapsNets, represent a pioneering stride in artificial neural networks. Their unique strength lies in capturing hierarchical relationships intrinsic to data. Traditional neural networks might overlook specific details in their processing, but CapsNets stand apart by meticulously preserving nuanced data aspects, encompassing spatial relationships and other intricate properties [28]. This retention of detail augments their potential, making them particularly favorable for specialized tasks such as dental caries detection. In such applications, understanding the spatial interplay between distinct tooth components becomes pivotal for accurate diagnosis, underscoring the significance of CapsNets [29].

#### **4.8 Federated learning**

Federated Learning represents a novel paradigm in machine learning that focuses on harnessing vast, decentralized data sprawled across numerous devices or servers to train models [30]. The essence of this approach is its inherent safeguard for privacy; AI models can be developed and fine-tuned without requiring raw data transfer. This becomes indispensable in sectors that handle sensitive information. For instance, in dental caries detection, federated learning stands out as an effective method. This technique can build models using data from dental clinics and hospitals [31]. This is especially remarkable because patient confidentiality remains unassailable even with such extensive data amalgamation, highlighting the immense potential of federated learning in privacy-centric scenarios.

#### **4.9 Multimodal learning**

Multimodal Learning is a sophisticated approach in machine learning that thrives on fusing data from diverse modalities, such as text, images, and sound, to refine predictions. This method showcases its strength in dental caries detection by potentially intertwining various strands of information. A typical multimodal learning model can deftly merge dental images with patient medical histories and other pertinent data, paving the way for more precise diagnostic outcomes and better forecasts of disease progression. By harnessing this multifaceted data, practitioners can achieve an enriched understanding, thereby enhancing the overall effectiveness of the diagnostic process [32].

The realm of AI in dental caries detection is poised for transformative progress. While each method has inherent strengths and drawbacks, the selection invariably hinges on the task, the data available, and the unique demands of the challenge. Often, amalgamating various methods can offer the most optimal outcomes. The convergence of burgeoning AI and machine learning innovations with the surge in dental imaging data indicates a trajectory towards highly precise, streamlined, and tailor-made diagnostic techniques. Beyond mere diagnostics, these state-of-the-art tools profoundly elevate the quality of patient care, ushering in a new era for dental health and treatment.

#### **5. Machine learning and image recognition in dental caries**

This "data-driven learning" approach has found significant application across the healthcare spectrum, notably in dentistry [33]. Historically, the management of dental caries, particularly its early detection and treatment, has been anchored in clinical evaluations and the interpretation of radiographic images. This method, though adequate to an extent, has its limitations. Subjectivity in interpretations and the potential oversight of nascent caries are notable drawbacks. Enter machine learning and the narrative changes. When exposed to a vast collection of dental images, machine learning algorithms can discern even the subtlest signs of dental caries

#### *Revolutionizing Dental Caries Diagnosis through Artificial Intelligence DOI: http://dx.doi.org/10.5772/intechopen.112979*

in their infancy. The efficiency of these models in analyzing and understanding a multitude of images within a short timeframe surpasses human capability, signifying a potential surge in the productivity of dental practices [34].

Supplementing this prowess of machine learning is image recognition. This facet of AI equips machines with a quasi-visual understanding, enabling them to dissect and infer data from images. When it comes to dental caries, this technology becomes invaluable. AI systems harness image recognition to meticulously scan dental radiographs or intraoral photographs, highlighting potential decay sites. The precision of this technology lies in its capability to differentiate between a healthy tooth structure and areas affected by caries, thus streamlining further clinical examinations. Integrating machine learning and image recognition crafts a formidable method for detecting dental caries. The fusion of these technologies ushers in an era of advanced diagnostic instruments characterized by remarkable accuracy and efficiency. It is pivotal to understand that the efficacy of such AI systems is directly proportional to the quality and variety of the training data. A machine learning model exposed to diverse dental images representing various caries stages and a broad patient demographic will likely be the gold standard in reliability.

However, the journey of integrating machine learning and image recognition into dental caries management is full of hurdles. The technological quandary of securing pristine input data and the enigmatic nature of some AI-driven decisions pose challenges. Add to this the ethical conundrums of patient consent and data privacy, and the complexity magnifies. Yet, with adequate safeguards and relentless research, these obstacles are surmountable. The alliance of machine learning and image recognition redefines how dental caries are detected and managed. This transformation promises enhanced diagnostic precision and paves the way for timely and efficacious interventions. As these technologies continue to evolve, it becomes imperative to periodically assess and hone them, ensuring they serve as invaluable adjuncts to the expertise of dental professionals, fostering superior oral healthcare.

#### **5.1 Image processing and analysis techniques for identifying caries lesions**

With the rise of digital imaging in dentistry, there has been a growing interest in leveraging image processing and analysis techniques to aid in the early detection and management of dental caries. Early identification of caries lesions can lead to timely intervention, potentially saving the patient from more extensive treatments in the future (**Figure 1**).

#### *5.1.1 Image acquisition*

The first step involves acquiring a high-quality image of the tooth or teeth. This could be done using various modalities such as intraoral cameras, digital radiographs, or optical coherence tomography. The quality of the image acquired significantly influences the subsequent analysis; thus, ensuring a clear, high-resolution image is imperative. The process begins with selecting an appropriate imaging modality [35]. Traditional dental radiographs, such as bitewings, periapical, and panoramic images, remain popular due to their comprehensive visualization capabilities. However, intraoral cameras have gained traction for offering a live, high-resolution glimpse into the oral cavity, highlighting potential early-stage caries or enamel anomalies. More recently, Optical Coherence Tomography (OCT) has emerged as a non-invasive technique that captures detailed cross-sectional views of dental structures, thereby aiding in the early identification of caries [36].

**Figure 1.** *Dental radiograph analysis and dental caries detection through artificial intelligence.*

Quality, especially resolution, is indispensable. While higher resolutions offer detailed insights, revealing even minute structural changes indicative of caries onset, they also pose storage and processing challenges due to larger file sizes. Consistency further accentuates image acquisition, especially when monitoring caries progression or conducting research. Factors like angle, lighting, and imaging settings must remain uniform. Image acquisition in dental caries detection transcends mere picture capturing. It is a meticulous procedure that, when executed with precision, promises an accurate and efficient diagnostic trajectory in the stages that follow.

#### *5.1.2 Pre-processing in dental caries image analysis*

The pre-processing stage holds pivotal significance in the dental caries image analysis continuum. It primarily bridges raw image acquisition and intricate analysis, ensuring the input images are refined and optimized for subsequent stages. Dental images, even when captured meticulously, often contain noise, artifacts, or variances in illumination, which can influence the accuracy of caries detection. Pre-processing techniques are designed to mitigate these imperfections and enhance the image's quality [37]. Common methodologies include:

**Noise Reduction:** Dental images, especially radiographs, can be subjected to random noise due to equipment, transmission errors, or external interferences. Techniques like Gaussian blurring, median filtering, and wavelet denoising are employed to smooth out the image, preserving the essential features while removing extraneous details.

#### *Revolutionizing Dental Caries Diagnosis through Artificial Intelligence DOI: http://dx.doi.org/10.5772/intechopen.112979*

**Contrast Enhancement:** The image's contrast can be amplified to better visualize dental structures and potential caries lesions. Histogram equalization and adaptive histogram equalization are standard techniques to adjust image contrast, emphasizing subtle differences between healthy and carious dental tissues.

**Normalization:** Inconsistencies in lighting or image acquisition settings across multiple images can be counteracted by normalizing the intensity levels. This ensures a consistent brightness and contrast range, facilitating comparability across various images.

**Segmentation:** Only some parts of an image are often relevant for caries detection. Pre-processing may involve segmenting the image, isolating the region of interest (e.g., a specific tooth or quadrant) from the background or adjacent structures. This streamlines the subsequent analysis, focusing only on pertinent areas.

**Resolution Scaling:** To manage computational efficiency, images may be rescaled to a standardized resolution, especially in AI or machine learning models. This harmonizes the input size for subsequent processing, although care must be taken to ensure that vital diagnostic details are not lost in the rescaling process.

**Artifact Removal:** Dental images can sometimes contain artifacts - extraneous features or distortions, often arising from equipment malfunctions, patient movement, or external objects (like metal restorations). These artifacts can interfere with accurate caries detection. Pre-processing aims to identify and mitigate these disturbances, ensuring the integrity of the diagnostic image.

The overarching objective of pre-processing is to curate an image that is void of distractions and optimized for clarity. It accentuates the relevant features, equips the image for efficient analysis, and sets the stage for accurate caries detection. As dental imaging evolves with technology's strides, the pre-processing phase will undoubtedly adapt, integrating more sophisticated techniques to ensure that the foundational image quality is preserved and enhanced for crucial diagnostic tasks.

#### *5.1.3 Feature extraction in dental caries image analysis*

Feature extraction is a crucial step in the image analysis process, especially in the context of dental caries detection. This phase involves isolating and quantifying pertinent attributes from the pre-processed image, instrumental in characterizing and distinguishing between healthy and carious dental tissues. These attributes or "features" become the foundation upon which classification models make decisions, especially in machine learning or AI-based systems.

#### *5.1.3.1 Texture analysis*

Dental caries often manifest changes in the texture of dental tissues. Techniques like gray-level co-occurrence matrix (GLCM), local binary patterns (LBP), and wavelet transforms are employed to extract texture-related features. These methods quantify variations in the image, capturing nuances that can signal the presence of early or advanced carious lesions [38].

#### *5.1.3.2 Shape and morphological features*

The contours and morphologies of carious lesions can be distinct from the regular dental anatomy. Edge detection algorithms, such as the Canny or Sobel operators, can help identify the boundaries of lesions. Once the edges are identified, shape

descriptors, like compactness, elongation, or roundness, can be calculated to provide insights into the lesion's characteristics.

#### *5.1.3.3 Intensity and statistical features*

The pixel intensity distribution within an image or a segmented region can offer valuable information. Mean, variance, skewness, and kurtosis of the pixel intensities can be computed. Carious regions might present with different intensity distributions compared to healthy tissues, making these features valuable for differentiation.

#### *5.1.3.4 Frequency domain features*

Transforming the image from the spatial domain to the frequency domain using methods like the Fourier Transform or Wavelet Transform allows the extraction of features that might not be discernible in the standard spatial domain. These frequency-based features can be susceptible to subtle changes caused by caries.

#### *5.1.3.5 Color-based features*

While more relevant for intraoral photographs than radiographs, the color attributes of an area can be informative. RGB (Red, Green, Blue) values, Hue-Saturation-Value (HSV) descriptors, or other color spaces can be analyzed to extract features differentiating healthy and carious enamel or dentin.

#### *5.1.3.6 Spatial relationships*

In some instances, the relative positioning of features within the dental anatomy can be insightful. For example, the proximity of a potential lesion to known anatomical landmarks, like the enamel-dentin junction, can provide context for the extracted features.

#### *5.1.3.7 Deep learning features*

With the advent of deep learning techniques, particularly convolutional neural networks (CNNs), feature extraction can also be automated. In these models, the initial layers often act as feature extractors, identifying and emphasizing attributes of the image that are crucial for caries detection.

Feature extraction transforms an image's rich, intricate details into a structured, quantitative format. These distilled features capture the essence of the image, allowing diagnostic models to efficiently and accurately differentiate between healthy and carious tissues. As image analysis technologies advance, the feature extraction phase will continue to evolve, incorporating more nuanced and sophisticated techniques that elevate the precision and reliability of dental caries detection.

#### *5.1.4 Classification in dental caries image analysis*

In dental caries image analysis, classification is the stage where the extracted features from the images are used to categorize or label the data, determining whether a particular region of interest in a dental image is healthy or carious. Given the complexity and variability of oral images, an effective classification system is pivotal for accurate diagnostics [39].

#### *5.1.4.1 Machine learning algorithms*

Traditional machine learning techniques have been widely employed for classification tasks in dental imaging. Algorithms such as Support Vector Machines (SVM), Decision Trees, Random Forests, and K-Nearest Neighbors (KNN) can be trained on labeled datasets to recognize patterns in extracted features and classify new, unseen images. Each algorithm offers a distinct approach. For example, SVM works by finding a hyperplane that best separates the classes, while Random Forests employ multiple decision trees to vote on the classification [40].

#### *5.1.4.2 Deep learning and neural networks*

In recent years, deep learning, particularly Convolutional Neural Networks (CNNs), has shown promise in dental image classification. CNNs are a subset of neural networks designed to learn spatial hierarchies of features automatically and adaptively from images. They can be trained end-to-end, meaning raw images can be input, and the network handles feature extraction and classification. Due to their depth and complexity, CNNs can capture intricate patterns in images, often outperforming traditional methods, especially when vast amounts of data are available.

#### *5.1.4.3 Ensemble methods*

These methods combine multiple classifiers to produce a final decision, typically yielding better performance than individual classifiers. Techniques like bagging, boosting, or stacking can be employed, pooling insights from different models for more robust classification.

#### *5.1.4.4 Evaluation metrics*

Once classification models are trained, their performance must be assessed. Metrics such as accuracy, precision, recall, F1-score, and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC) are used. These metrics give insights into the model's actual positive rate, false positive rate, false negatives, and more, facilitating the choice of the most effective classifier for a particular application.

#### *5.1.4.5 Regularization and overfitting*

One challenge in classification is overfitting, where the model performs exceptionally well on training data but needs to generalize on unseen data. Techniques such as dropout, L1 or L2 regularization, and early stopping are implemented to ensure robust models generalize well to new data.

#### *5.1.4.6 Transfer learning*

Given the challenges in obtaining vast amounts of labeled dental images, transfer learning has emerged as a beneficial approach. Pre-trained models trained on large datasets like ImageNet are fine-tuned on smaller, domain-specific datasets, leveraging the knowledge acquired during the initial training phase to enhance performance in dental caries classification.

#### *5.1.4.7 Continuous learning and model updates*

Given the evolving nature of dental imaging and the continuous accrual of new data, classification models should be designed for periodic updates. This ensures they remain current and maintain high-performance levels as new patterns or variations in dental caries emerge.

The classification step in dental caries image analysis is the culmination of all preceding stages. It determines the final diagnostic output, labeling regions as carious or healthy. With the rapid advancements in machine learning and deep learning, classification algorithms are becoming increasingly sophisticated, paving the way for more accurate, efficient, and reliable dental diagnostics.

#### *5.1.4.8 Post-processing*

Once the potential caries lesions are identified, post-processing techniques can be used to refine the results. This might involve removing any small, isolated regions identified as caries (which might be false positives) or filling in small gaps in detected caries regions.

#### *5.1.5 Visualization and interpretation*

The final processed image is then visualized, highlighting areas detected as caries lesions. Dental professionals can interpret these highlighted areas with clinical knowledge, ensuring the results align with clinical observations and radiographic findings [41]. Image processing and analysis techniques provide a systematic and efficient approach to identifying caries lesions. When combined with the expertise of dental professionals, these techniques can significantly enhance the accuracy of caries detection, leading to better patient outcomes. As technology and algorithms evolve, integrating such techniques in everyday dental practice is set to become more prevalent, revolutionizing the early detection and management of dental caries.

#### **6. Various AI based methods used in the detection of dental caries**

#### **6.1 Deep learning for proximal caries detection in bitewings**

Proximal dental caries lesions are intricate cavities that develop on the surfaces where teeth come into contact. Their concealed location often makes early detection challenging, leading to a reliance on bitewing radiographs. These radiographs have historically been essential, delivering an in-depth view of the hidden proximal surfaces, including both the maxillary (upper) and mandibular (lower) teeth crown sections. Despite the significant reliance on these radiographs, traditional methods, characterized by visual evaluations and manual image analysis, are susceptible to human subjectivity and potential oversights. The advent of artificial intelligence (AI) and its integration into the healthcare sector heralded a seismic shift in the diagnostic approach to these dental challenges [42]. Deep learning, a sophisticated subset of machine learning, employs multi-layered artificial neural networks to interpret intricate patterns within extensive datasets. These neural networks emulate the complexity and interconnections observed in human neural circuits. In dentistry, deep

#### *Revolutionizing Dental Caries Diagnosis through Artificial Intelligence DOI: http://dx.doi.org/10.5772/intechopen.112979*

learning algorithms, especially Convolutional Neural Networks (CNNs), are adept at scrutinizing bitewing radiographs with unmatched precision. The primary aim is to detect proximal caries [43] accurately.

The efficacy of these algorithms is contingent upon rigorous training. An extensive, annotated collection of bitewing images, meticulously marked by seasoned dental experts to indicate caries' presence or absence, serves as the foundational dataset. These models can adeptly analyze new, unlabeled radiographs upon exhaustive training, identifying potential carious patterns based on their learned knowledge [44]. Integrating deep learning offers dual advantages: swift image analysis and mitigating interpretative discrepancies or errors from human fatigue or inherent subjectivity. Contemporary research underscores the unparalleled proficiency of deep learning models, emphasizing their exceptional sensitivity and specificity in detecting proximal caries on bitewing radiographs. In numerous instances, these models rival or surpass the diagnostic acumen of experienced dental professionals [43]. Incorporating deep learning techniques marks a transformative epoch in dental radiographic analysis. By harnessing these avant-garde algorithms, dental practitioners can significantly enhance the precision and efficiency of their diagnoses, culminating in improved patient outcomes and streamlined clinical workflows.

#### **6.2 Deep learning for ICDAS™ classification in bitewing radiographs**

The ICDAS™ system, an internationally recognized standard, has ushered in a new era of caries detection and assessment [45]. But with the surge of technological advances, particularly the integration of artificial intelligence, we are on the brink of a revolution in dental diagnostics. Deep learning, characterized by its use of intricate artificial neural networks, can delve deep into extensive datasets, analyzing multiple hidden layers and recognizing nuanced patterns that may elude the human eye. The potency of this technology becomes palpable when applied to bitewing radiographs, the cornerstone of dental imaging. Here, the aim is not just detection but the precise classification of dental caries according to the rigorous benchmarks set by the ICDAS™ system. This level of granularity in diagnostics is pivotal, as it facilitates more personalized treatment plans and proactive interventions.

Central to this endeavor are Convolutional Neural Networks (CNNs). Their architecture makes CNNs uniquely equipped to process visual data, making them the model of choice for imaging-based diagnostics. However, the true efficacy of a CNN is contingent on the quality and comprehensiveness of its training data. An annotated with ICDAS™ scores by expert dental practitioners, a meticulously curated dataset lays the foundation. The rigorous training regimen involves the CNN making predictive inferences on caries classifications, juxtaposed with actual ICDAS™ scores. Discrepancies between predicted and actual scores serve as a feedback mechanism, activating backpropagation algorithms [22]. These algorithms adjust the CNN's parameters, fine-tuning its predictive prowess in a relentless pursuit of diagnostic perfection. Upon satisfactory training, CNN's capabilities are put to the test. Presented with the novel, unlabeled bitewing radiographs, it undertakes the task of discerning and classifying dental caries, assigning them ICDAS™ scores [22]. This marriage of deep learning technology with the ICDAS™ system is emblematic of the future of dental diagnostics — one where precision, consistency, and technology converge to optimize patient outcomes.

#### **6.3 Dental radiographic segmentation with neutrosophic logic**

Segmentation of dental radiographic images is pivotal for effective dental caries diagnosis. Traditional methods often grapple with noise and image uncertainties. However, neutrosophic logic presents an innovative solution [46]. Conceived by Florentin Smarandache, this logic comprehends imprecision typical of human cognition and tangible scenarios, surpassing the binary logic's confines of strict truth and falsehood by introducing indeterminacy. When applied to image processing, neutrosophic logic capably navigates radiographic ambiguities. The initial step entails transforming the image to the neutrosophic domain. Pixels are defined by three sets: truth (T), falsehood (F), and indeterminacy (I). These sets reflect the pixel's affiliation to the object (like tooth structure), non-affiliation, and uncertain status [47].

After this transformation, neutrosophic-centric image enhancement methods amplify contrast and minimize noise, heightening structure visibility. Segmentation then employs neutrosophic-designed techniques, utilizing membership degrees within the T, F, and I sets for pixel classification. This neutrosophic segmentation strategy touts several merits, notably its capacity to adeptly manage dental radiographic image uncertainties adeptly, resulting in consistent, precise segmentations, thus refining subsequent analytical procedures [48]. However, it demands significant computational resources, as each pixel requires a three-set analysis. Additionally, selecting optimal parameters for image amplification and segmentation can be intricate, potentially necessitating expert insight. Nevertheless, neutrosophic logic in dental radiographic image segmentation offers a groundbreaking, dependable methodology, propelling advancements in dental ailment detection and diagnostics.

#### **6.4 YOLOv3 and ICCMS™ in bitewing radiographs**

YOLO, standing for "You Only Look Once," represents the forefront of object detection in computer vision. Its third version, YOLOv3, offers unmatched speed and accuracy, opening new doors across various detection scenarios. One field ripe for innovation is dentistry, where YOLOv3's capability for accurately detecting and classifying dental caries is now being explored. YOLOv3's adaptation is based on the comprehensive ICCMS™ (International Caries Classification and Management System) radiographic scoring system [49].

YOLOv3, a deep-learning gem, is designed for rapid and sharp object detection. Its efficiency stems from its regressive method, marking objects and predicting class probabilities straight from an image in one step. To align YOLOv3 with dental caries detection, it is trained on a vast bitewing radiographic dataset annotated with caries details and matching ICCMS™ scores. As YOLOv3 trains, it recognizes the visual markers linked to different stages of caries as classified by ICCMS™. In practice, the trained model identifies and matches caries with ICCMS™ scores, annotating a radiograph with accurate caries borders and classifications [50]. YOLOv3's effectiveness in this application is gauged using metrics like precision, recall, F1-score, and mAP, which collectively shed light on its skill in identifying and categorizing dental caries. However, the pathway to embed YOLOv3 in dental diagnostics is challenging. Collecting an extensive, diverse, and well-annotated training dataset is a significant hurdle. YOLOv3's inherent "black box" nature, typical of many deep learning models, can raise questions about its decision-making transparency. This method emphasizes YOLOv3's transformative capability in dental caries detection, particularly its

alignment with the ICCMS™ system. While obstacles remain, the convergence of YOLOv3 and ICCMS™ paints a bright picture for the future of dental diagnostics [51].

#### **6.5 Blob detection in dental caries diagnostics**

The dawn of blob detection in dental diagnostics represents a paradigm shift in how dental caries are identified and diagnosed. This technique, rooted in image processing, recognizes and distinguishes regions termed 'blobs' based on their unique brightness attributes. In dental radiographs, these blobs are often discerned as shadowy indicators of dental caries, contrasting vividly against the healthier parts of tooth structures [52]. The merits of blob detection in dentistry are multifaceted. Firstly, its precision is noteworthy. The technique's ability to pinpoint even the most nascent lesions facilitates prompt interventions, potentially halting further tooth damage. Then comes the aspect of efficiency. Traditionally, dental professionals might invest significant time in meticulously examining radiographs. With blob detection, this analysis is streamlined, allowing dentists to channel more of their time and focus on direct patient care. Another pivotal advantage is the promise of consistency. By shifting diagnosis to an algorithm-driven approach, the interpretation process sidesteps individual professionals' personal biases and nuances, ensuring a standardized evaluation.

The procedure of blob detection in dental diagnostics unfolds systematically. Initially, radiographs are subjected to pre-processing, where they undergo optimization for contrast and noise reduction. This stage is foundational, priming the images for the rigorous blob identification that follows. The actual process of blob detection sees specialized algorithms deployed to scan the radiograph meticulously. The objective is to highlight areas exhibiting pronounced brightness disparities. As these darker regions come into focus, they are typically flagged as indicators of decay [53]. The concluding phase, post-processing, plays a critical role. Here, the detected blobs are assessed for validity. Determinations are drawn regarding whether they genuinely represent caries, gauging their severity and even predicting potential progression trajectories based on the characteristics of these blobs.

A fascinating frontier in this domain is the burgeoning synergy between blob detection and machine learning. Merging the pinpoint accuracy of blob detection with the dynamic adaptability of machine learning paints a promising picture for dental diagnostics. The combined prowess promises speed and heightened accuracy, and uniformity in diagnosis. And while integrating these technologies is not without its challenges, their combined potential to redefine the landscape of dental care is undeniably vast. As a relatively recent entrant in dental diagnostics, blob detection is rapidly becoming an invaluable tool [54]. With its continued integration with technologies like machine learning, the future of dental diagnostics and care seems poised for significant innovation and patient-centric advancements.

#### **6.6 Dental caries detection with meta-heuristic-based ResNeXt-RNN**

The innovative fusion of the ResNeXt-RNN model emerges as a trailblazer in dental caries detection. Building on the foundational prowess of deep learning, the ResNeXt architecture distinguishes itself in managing high-dimensional data sets, especially intricate images. A breakthrough conceptualized by Ramana Kumari, Nagaraja Rao, and Ramana Reddy [55], ResNeXt introduces "cardinality" as an added dimension, augmenting the conventional depth and width dimensions that

define neural architectures. But the genius does not stop here. The Recurrent Neural Network (RNN) component is integrated into this image processing. Historically recognized for its aptitude in managing sequences and retaining memory states, RNN offers the ability to analyze temporal dynamics, a particularly crucial facet for assessing the progression of dental conditions over time.

The synergy of ResNeXt and RNN culminates in a model that excels on dual fronts. While the ResNeXt component meticulously captures spatial intricacies, the RNN counterpart evaluates the temporal evolution of dental caries. Elevating this amalgamation further is the integration of meta-heuristic algorithms. These advanced computational blueprints enhance and guide models' learning trajectories, amplifying their overall performance. The methodology unfolds systematically [56]. Dental images are first channeled into the ResNeXt component, which performs the initial feature extraction phase, identifying critical markers that might indicate the presence of caries. These features, once isolated, are directed to the RNN module, which assesses their temporal dynamics – invaluable insights, particularly for studies tracking the longitudinal progression of caries. Simultaneously, the inherent meta-heuristic algorithm plays its part in fine-tuning and optimizing the model's learning parameters.

While the merits of this integrated approach are manifold, promising a panoramic and dynamic view of dental caries detection, it is full of challenges. The complexity inherent in the model might raise interpretability issues. Validating its decisions could become an intricate task. Additionally, sourcing a rich repository of high-quality dental images for training is not only a logistical challenge but also broaches concerns related to patient privacy. Yet, optimism and anticipation are the overarching sentiments surrounding the meta-heuristic-based ResNeXt-RNN model. Its unparalleled capabilities in dental image analysis herald a potential paradigm shift, particularly for early-stage caries detection. As the confluence between AI and dentistry deepens, it is becoming evident that models like these will profoundly shape the future contours of oral health diagnostics.

#### **6.7 PaXNet: Ensemble and capsule classifier for panoramic X-ray caries detection**

The application of artificial intelligence (AI) in dentistry has been transformative. One standout model demonstrating its prowess in this domain is PaXNet. This innovative technique leverages Ensemble Transfer Learning and the Capsule Classifier to optimize dental caries detection in panoramic X-rays. PaXNet is a custom-built AI model to identify dental caries using panoramic X-rays [57]. Its two-fold system magnifies the advantages of Ensemble Transfer Learning and Capsule Networks. The former, Ensemble Transfer Learning, amalgamates insights from various pre-trained deep learning models, re-purposing them for dental caries detection. Such integration ensures that the model extracts the most salient features across multiple datasets, augmenting its predictive prowess and adaptability. Conversely, Capsule Networks (CapsNets) introduce a revolutionary neural network structure. They retain nuanced information about the spatial hierarchies and relationships within the features of an image. A Capsule Classifier, built atop the CapsNets, classifies images based on the calculated probabilities of specific feature presence [58].

Within PaXNet, Ensemble Transfer Learning takes the lead, teasing out relevant features from panoramic X-ray captures. Subsequently, these features undergo evaluation by the Capsule Classifier, determining the presence or absence of dental caries. A notable advantage of PaXNet is its capability to identify intricate feature interrelationships within panoramic X-ray images—details that often elude conventional

#### *Revolutionizing Dental Caries Diagnosis through Artificial Intelligence DOI: http://dx.doi.org/10.5772/intechopen.112979*

convolutional neural networks. This intrinsic strength translates to enhanced accuracy and precision in caries detection. Empirical evidence supports PaXNet's prowess. Comparative studies have underscored its superior sensitivity, specificity, and accuracy vis-à-vis other machine learning paradigms. Given its formidable performance metrics, PaXNet is preferred for dental healthcare setups. It offers a swift and dependable adjunct opinion on dental caries, streamlining diagnostic processes. PaXNet's synergistic utilization of Ensemble Transfer Learning and Capsule Classifier signifies a watershed moment in dental caries detection from panoramic X-rays [27]. Embracing its potential and mitigating its challenges, we can anticipate PaXNet to redefine dental diagnostic protocols, fostering enhanced patient care trajectories.

#### **6.8 Pervasive deep gradient-based LeNet**

The transformative "Pervasive Deep Gradient-Based LeNet Classifier" stands as a beacon in the evolving landscape of dental caries detection, signaling a departure from traditional diagnostic methods. Initially designed by Yann LeCun for character recognition, the LeNet structure, with its specialized convolutional layers, has proven invaluable in processing dental imagery and capturing intricate spatial data hierarchies. Transitioning seamlessly through its layers, from convolutional and pooling stages to a SoftMax classifier, LeNet provides a comprehensive image analysis.

In dental diagnostics, this specific iteration of LeNet has been meticulously tailored. Equipped with an uncanny ability to parse nuanced patterns within data, it scrutinizes dental images precisely, distinguishing pivotal features and subsequently classifying them based on the presence or absence of caries [59]. The model's integration of the "pervasive deep gradient-based" methodology is particularly noteworthy. This technique employs gradient descent, a cornerstone of machine learning optimization, to iteratively refine the model's parameters, ensuring its diagnostic accuracy improves with each training cycle. Among its salient strengths, the model boasts an unparalleled prowess in data management. It can deftly navigate extensive dental image datasets, rendering manual feature extraction a relic of the past. Its operational speed and diagnostic precision allow for swift and accurate disease identification, paving the way for timely and effective treatments. Furthermore, its heightened sensitivity ensures the detection of even the most subtle structural tooth changes, often identifying caries in their initial stages when they are most amenable to treatment. The introduction of the "Pervasive Deep Gradient-Based LeNet Classifier" into the field of dental diagnostics heralds a new era, one in which carries detection becomes increasingly efficient, predictive oral health measures are refined, and overall patient care standards soar to new heights [60].

#### **6.9 AssistDent®: aI-assisted dental diagnostics**

AssistDent® is a cutting-edge AI software tailored for precise dental caries detection. Utilizing deep learning algorithms, particularly Convolutional Neural Networks (CNNs), it excels in analyzing dental images, ensuring dental professionals can make informed decisions quickly and accurately. The strength of AssistDent® stems from its ability to scrutinize dental radiographs with unparalleled precision. These capabilities arise from extensive training on numerous dental radiographs annotated by seasoned professionals. As a result, the early stages of dental caries are quickly identified, making early intervention feasible. The software's intuitive interface seamlessly integrates into a dental clinic's workflow, offering real-time feedback

that revolutionizes the diagnostic process, giving practitioners more time with their patients. Rigorous tests confirm AssistDent®'s exemplary sensitivity and specificity in caries detection, further establishing its role as an indispensable tool for dental diagnostics [61, 62].

However, like most AI tools, AssistDent® has its challenges. Its efficacy is largely contingent upon the quality of its training data, emphasizing the need for ongoing updates and training with diverse datasets. Efforts to enhance its transparency and interpretability in decision-making are continuous. Looking to the future, the potential for AssistDent® extends beyond merely caries detection. As AI evolves and datasets grow, it is poised to become a holistic diagnostic powerhouse for a spectrum of oral conditions. In essence, AssistDent® embodies the next giant leap in AI-enhanced dental care, pointing towards a future where diagnostics are more refined, swift, and universally accessible.

#### **6.10 Bitewing radiographs: deep learning caries classification with ICDAS™**

Deep learning algorithms have seen a rising application in identifying and classifying dental caries in bitewing radiographs, mainly using the globally acknowledged ICDAS™ (International Caries Detection and Assessment System) radiographic scoring. This system standardizes caries detection, allowing more streamlined communication among dental professionals. Within the ICDAS™ framework, dental caries is scored on a spectrum from 0 to 6, with 0 denoting sound tooth surfaces and six pointing to pronounced cavities [63]. Deep learning, an advanced branch of machine learning, employs artificial neural networks with numerous hidden layers, enabling intricate pattern recognition from vast datasets. Within dental diagnostics, this technology can be tailored to sift through bitewing radiographs and categorize dental caries based on the ICDAS™ criteria. Convolutional neural networks (CNNs), a type of deep learning model, play a pivotal role due to their aptitude for visual data analysis, making them ideal for image-centric tasks.

The initial step to developing such a model demands a comprehensive dataset of annotated bitewing radiographs. Expert dental professionals must meticulously label each radiograph with its corresponding ICDAS™ score. During the training phase, the CNN evolves, recognizing radiographic patterns that align with each specific ICDAS™ grade [64]. This learning is facilitated by a mechanism known as backpropagation. Here, the model's predictions are juxtaposed against actual labels, and subsequent adjustments are made to minimize discrepancies. After this rigorous training, the model can autonomously assign ICDAS™ scores to previously unanalyzed bitewing radiographs.

#### **7. Ethical and clinical considerations**

A significant cornerstone of any AI system is the vast troves of data it feeds on. This encompasses a wide range of patient-specific data in dentistry: from dental records and radiographic images to detailed medical histories. Acquiring this data mandates an informed consent process, where patients are fully aware of their data's utilization, storage, and potential risks. The subsequent storage of such critical information raises concerns about encryption, unauthorized access, and potential breaches. In today's interconnected digital era, data often traverses various platforms, sometimes extending to third-party vendors. This sharing must stringently align with

#### *Revolutionizing Dental Caries Diagnosis through Artificial Intelligence DOI: http://dx.doi.org/10.5772/intechopen.112979*

privacy regulations like GDPR or HIPAA, ensuring uncompromised patient confidentiality. Moreover, anonymizing data for AI model training can further mitigate identification risks.

The potential of AI in revolutionizing dental care pivots on its accuracy and reliability. The backbone of an accurate AI system is the quality of its training data, which must be comprehensive, varied, and genuinely representative of diverse patient demographics to prevent inherent biases and inaccuracies. Rigorous validation processes, where AI outcomes are juxtaposed against traditional diagnostic results, are imperative to vouch for the system's consistency. A challenge often surfaces with AI is its opaque "black box" algorithms, which can hinder a field where traceability and understandability of diagnoses are crucial [65]. Thus, fostering transparency and promoting an environment where AI acts as an assistant to human expertise rather than a replacement can be instrumental. The horizon of AI in dental caries management is undeniably promising. However, its seamless and efficacious integration into the dental sphere demands meticulous navigation of the intertwined ethical and clinical pathways. Addressing these considerations will maximize AI's potential and elevate the dental care standard, blending technology and human expertise in a harmonious symphony.

#### **8. Limitations and challenges**

The infiltration of Artificial Intelligence (AI) into the intricate dental caries diagnosis and management domain promises a transformative impact. But like any innovation, AI faces its set of hurdles. These challenges, ranging from technical to human, underscore the need for careful deployment of AI tools while maintaining a balanced perspective on their role in dental care. One predominant challenge is the variability in AI-driven results. AI models, especially when delving into nuanced realms like dental caries detection, may sometimes produce inconsistent or variable outcomes, depending on the algorithm's design or the data it was trained on. These inconsistencies could lead to missed diagnoses or false positives, which can have considerable ramifications in the sensitive patient care environment. Clinicians might find themselves at crossroads, especially when the AI output deviates from their clinical judgment, potentially leading to decision-making dilemmas [16].

At the heart of any effective AI system lies its data – the more robust and diverse, the better. However, AI's dependence on the quality and quantity of data presents a significant challenge. Incomplete, biased, or unrepresentative datasets can skew AI outputs, rendering them less accurate or misleading. In dental caries, where variances can be subtle, training AI models on limited or non-diverse data can gravely compromise their detection capabilities [66]. The quest for substantial, high-quality data is thus not just a technical requisite but a critical determinant of the AI system's clinical utility. But perhaps one of the most profound challenges springs not from technology but from its human stakeholders. The dental community, built on years of rigorous training and honed expertise, might view AI's advent with skepticism or even resistance. The essence of dental care goes beyond mere diagnoses – it encompasses the human touch, experience-based intuition, and a relationship of trust with patients. To many in the dental fraternity, relegating some of these responsibilities to an algorithm might seem disconcerting. Striking a harmonious balance between human expertise and automation is essential. AI should be perceived not as a replacement but as a tool – an adjunct that augments the dentist's capabilities, making their practice more

efficient and precise. The promise held by AI in reshaping dental caries management is immense, but it is crucial to approach this frontier with cognizance of its limitations [67]. The true potential of AI in dental care can be realized by addressing these challenges head-on and fostering a collaborative spirit between technology and human expertise.

#### **9. The future of AI in dental caries management**

The relentless march of technological progress, coupled with the ever-evolving field of artificial intelligence (AI), heralds an exciting new era for dental care. As AI continues to weave its way into myriad aspects of healthcare, dental caries management stands to benefit enormously. The current landscape of dental caries management has already seen revolutionary changes with the integration of AI, especially in detection. The future, however, is even more promising. Advanced AI algorithms are projected to achieve unparalleled accuracy in spotting early signs of dental caries, reducing the chances of false negatives, and ensuring timely interventions. As AI systems continue to 'learn' from an expanding database of dental images and case studies, their predictive capabilities will be refined further. This implies that, in the future, AI might not just identify existing caries but also predict potential decay based on a combination of the patient's oral history, habits, and genetic predispositions [68].

Moreover, management tools are expected to evolve, integrating real-time feedback mechanisms. Imagine a scenario where, during a dental procedure, an AI-powered tool offers real-time guidance to a dentist, suggesting optimal interventions based on the analysis of thousands of similar cases. Such advancements can drastically reduce procedural errors and improve treatment outcomes. AI's potential is not restricted to stand-alone applications. Its true power might be unleashed when integrated with other emerging dental technologies. Integrating AI with tele-dentistry platforms can democratize dental care, especially in remote regions. AI-powered diagnostic tools can offer preliminary assessments, guiding patients in areas without immediate dental expertise and suggesting when to seek advanced care. The future seems bright as AI matures and finds its footing in dental care. From advanced detection tools to create a comprehensive, patient-centric care model, AI promises to reshape the fabric of dental caries management, steering it towards excellence and holistic well-being.

#### **Conflict of interest**

The authors declare no conflict of interest.

*Revolutionizing Dental Caries Diagnosis through Artificial Intelligence DOI: http://dx.doi.org/10.5772/intechopen.112979*

#### **Author details**

Sukumaran Anil1 \*, Krishnaa Sudeep2 , Sudeep Saratchandran2 and Vishnupriya K. Sweety3

1 Department of Dentistry, Oral Health Institute, Hamad Medical Corporation, Doha, Qatar

2 PMS College of Dental Science and Research, Trivandrum, Kerala, India

3 Pushpagiri Institute of Medical Sciences and Research Centre, Kerala, India

\*Address all correspondence to: drsanil@gmail.com

© 2023 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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#### **Chapter 4**

## The Use of Fluoride for Enamel Caries Management in Infants

*Ana Cláudia Rodrigues Chibinski, Anna Bárbara Maluf, Larissa Yumi Ito, Letícia Maira Wambier, Mayara Vitorino Gevert and Vitória Monteiro*

#### **Abstract**

The presence of one or more decayed surfaces (cavitated or non-cavitated), lost or restored (due to caries) in any primary tooth in a child under 6 years old is considered Early Childhood Caries (ECC). Therefore, as soon as an initial enamel caries lesion is detected in a primary tooth, adequate measures must be adopted to halt the progression of this lesion into a cavity. To achieve this objective, fluoridated products are the most common resource, being available worldwide. Considering the age group, the use of fluoridated toothpaste and fluoride varnishes are indicated as simple and effective preventive and therapeutic methods. This chapter will discuss the advantages of these methods based on contemporary scientific evidence, as well as their expected clinical results when properly indicated and used.

**Keywords:** fluorides, toothpastes, fluoride varnishes, infants, dental caries

#### **1. Introduction**

It is expected that more than 530 million children around the world have untreated caries lesions, with the largest number of these lesions concentrated at the age of 5 years old [1]. These lesions are the clinical sign of Early Childhood Caries (ECC), defined as "the presence of one or more decayed surfaces (cavitated or noncavitated), lost or restored (due to caries) in any primary tooth in a child under 6 years old" [2]. Specifically, in America, data from a systematic review showed a 48% prevalence (CI 95% 42–54) of ECC [3].

Therefore, any clinical evidence of disease activity in babies may be classified as ECC, since a white spot can evolve to cavitation. So, different strategies must be adopted, which include effective procedures for controlling lesions in their different stages of development. Ideally, this disease management should occur before cavitation, preventing the need for restorative treatment and maintaining the integrity of the dental structure.

Considering current knowledge about cariology, the treatment can be done through non-invasive procedures and carious lesions can be controlled before cavitation (primary prevention) [2]. These procedures offer the patient a "friendly"

treatment for the child and tooth that prevents further loss of tooth structure, associating home care performed by parents/caregivers with in-office professional care. The aim is to adapt hygiene and diet to control the dysbiosis responsible for dental demineralization, associated with non-invasive methods to remineralize active lesions.

The prevention and treatment of caries disease involve the use of fluoride, which is a substance that interferes in demineralization-remineralization dynamics (DES-RE process). Therefore, fluoride is considered "a therapeutic agent that acts by controlling the beginning and development of caries lesions from the pre-cavitated stage of lesions formation" [4]. After topic application, the fluoride may be present in 5 different sites in the mouth: (1) biofilm and saliva; (2) incorporated into hydroxyapatite crystals into fluorapatite-like crystals; (3) enamel fluid; (4) adsorbed into hydroxyapatite surface, and (5) in the form of calcium fluoride (CaF2) [5]. Furthermore, its action is based on the constant presence of fluoride in the mouth, acting to reduce the demineralization and favoring the remineralization of the tooth. Therefore, the benefit is achieved by local and not systemic action [5], which is why oral fluoride supplementation is not recommended.

The fluor may be administered collectively to the population (fluoridation of public water supply, fluoridated salt, fluoridated milk), individually for selfapplication (fluoridated toothpaste, fluoridated solutions for mouthwashes), and exclusively for professional use (gels, foams, varnishes, slow-release devices, and fluoride-releasing restorative materials).

The use of fluoride agents in children and adolescents was analyzed by different systematic reviews, and it was concluded that toothpastes [6], fluoride mouthwashes [7], fluoride gel applications [8] and fluoride varnishes [9] are effective in reducing the incidence of caries.

Notwithstanding, not all forms of fluoride administration may be used in babies, safely, for prevention and caries treatment. Despite the patient's risk, fluoridated water and fluoridated toothpaste should be a constant in the baby's daily life. However, additional methods should only be indicated by a professional if an imbalanced condition is present in the baby's oral cavity. Fluoride gels, foams, and mouthwashes are not indicated for use in babies due to the possibility of inadvertent ingestion of the product.

Therefore, in this chapter, the discussion is focused on fluoride toothpaste, the main individual method, and fluoride varnishes, the main professional method, for the use of fluoride in infants.

#### **2. Fluoride toothpaste**

The understanding that caries is a biofilm-dependent disease can justify that toothbrushing is an essential process to control dental caries, using the association of systematic disorganization of the biofilm (toothbrushing) with daily topical application of the fluoride present in the toothpaste.

Strong evidence supports the use of fluoride toothpaste for caries prevention [10] with a minimum concentration of 1.000 ppm of Fluor [6]. This protocol was recommended in the Bangkok Declaration to reduce the prevalence of ECC [2].

In addition to the fluoride concentration in the toothpaste, other factors can also influence the use of toothbrushing in the prevention of caries. The best results are obtained with at least two brushings a day [2], and the toothbrushing before bedtime is the most important one because there is a reduction in salivary flow during sleep. It is also advisable not to rinse with water after brushing, as it may reduce the amount of fluoride in the oral cavity.

#### **2.1 Fluoride toothpastes and acute toxicity**

The probably toxic fluoride dose is 5.0 mg F/kg of body weight. Therefore, a 2-year-old child weighing approximately 12 kg would only be at risk of acute toxicity if he/she ingests 60 g of fluoride toothpaste with a concentration of 1.000 ppm F, which is more than the whole amount in a tube of toothpaste commonly sold (50 mg) [11]. So, although it is a possibility, it is a quite remote one. Conversely, chronic toxicity (fluorosis) occurs more frequently and it is what will be discussed below.

#### **2.2 Fluoride toothpastes and fluorosis**

Children under 5 years old have not yet developed the expectoration reflex and they swallow most of the fluoride in the toothpaste used during toothbrushing. For this reason, for a long time, fluoridated toothpaste and fluoridated water were considered responsible for the prevalence of fluorosis, and the recommendation was to use fluoride-free toothpaste for babies [12].

Currently, the importance of fluoride toothpaste in daily oral hygiene procedures is known, but the dose used in each brushing must be controlled (**Table 1**), since the amount of toothpaste swallowed is inversely proportional to the child's age and directly proportional to the distribution of toothpaste in the toothbrush [14].

The use of fluoridated dentifrices in babies is based on the concept that the benefit in controlling dental caries is obtained with a minimum risk for the development of fluorosis. There is a "window of susceptibility" for the occurrence of dental fluorosis,


#### **Table 1.**

*Use of fluoride dentifrices with 1.100 ppm in the first years of life and the risk of dental fluorosis.*

which is the period that coincides with enamel formation. For most permanent teeth, this occurs in the first 6 to 8 years of a child's life, with the exception of permanent incisors, when it corresponds to the first 3 years of a baby's life [15]. Therefore, since the birth of the first deciduous tooth, when brushing with fluoridated dentifrices is implemented, until approximately 8 years old, constant supervision is important to minimize swallowing while maintaining effective exposure to fluoride. Using the correct amount of toothpaste on the toothbrush, even if it is ingested (which is common in babies), the daily limit of systemic fluoride needed to develop fluorosis is not reached (**Table 1**).

Association with additional sources of fluoride like fluoridated water, may result in levels capable of causing fluorosis. However, it has been observed in most of the studied populations that if fluorosis develops, it is very mild, with little importance for the esthetics and quality of life of patients [16].

An important advice during this step is to guide parents/caregivers about the correct amount of toothpaste in each brushing according to the baby's age group. Often, the description used by the dentist may not be correctly interpreted by parents/caregivers, as demonstrated in a recent study [17]. These parents/caregivers tend to use larger amounts of toothpaste as the baby grows. Also, they tend to use significant variations from the ideal portion, which may interfere with the preventive effect of the dentifrice or have a greater influence on the development of fluorosis. Such observations show the importance of adequate guidance for parents/caregivers, preferably demonstrating the ideal amount with a toothbrush and toothpaste.

#### **3. Fluoride varnishes**

In this topic, the use of fluoride varnishes for the prevention of carious lesions and the treatment of initial lesions in enamel is discussed. This is not the first choice of dental material for the treatment of dentin lesions, but it may be considered a good treatment option for enamel lesions, mostly white spot lesions.

According to the American Dental Association (ADA), in addition to silver diamine fluoride (SDF) [18], fluoride varnishes are the only products for professional topical application recommended for use in patients younger than 6 years old [19].

The main advantages of fluoride varnishes are the prolonged contact time with the dental tissue, which favors the absorption of the ion and the formation of calcium fluoride (CaF2) reservoirs, and the possibility of using reduced amounts of the product, minimizing the possibility of ingestion and making it safer for use in babies.

The traditional formulation consists of a natural resin matrix (colophony) with a high concentration of sodium fluoride (22.600 ppm, 2.26% F- ions, or 5% NaF).

The natural resin base forms a film on the tooth surface and, once NaF is exposed to the oral cavity, it dissolves and solubilizes, forming CaF2 reservoirs. The reaction continues for periods up to 24 hours, which is favored by the contact of the varnish with the tooth, because it is time/contact-dependent. Therefore, it is recommended that patients treated with fluoride varnishes remain without brushing their teeth for different periods [20], which can vary from 45 minutes after application to 24 hours. This procedure not only increases the amount of CaF2 formed, but also favors remineralization over time [20].

#### **3.1 Effectiveness of fluoride varnish for prevention and treatment of caries disease in babies**

Varnishes can be applied to a specific carious lesion, where they will have a therapeutic/remineralizing effect, or to the entire dentition for a preventive effect.

Analyzing randomized clinical trials that studied the preventive effect of fluoride varnishes applied twice a year, it was observed that, in babies aged 18 to 47 months old, the preventable fraction was 31%, namely, there is 31% more chance that babies who received varnish treatment will not develop caries lesions when compared to those who did not receive the treatment [21]. On the other hand, with the same frequency of application, treatment with varnish in patients aged 2 to 5 years old had a preventive effect similar to supervised toothbrushing [22]. Systematic reviews on this topic are also available. The first one shows that the estimated preventable fraction in patients who received the application of fluoride varnish is 37% (95% CI 25–51%) when compared to placebo or no treatment and, therefore, there is a protective effect in deciduous teeth [9]. The second, considering the same comparison, found a protective effect of varnish on the tooth surface, but with questionable clinical relevance, concluding that the preventive effect of fluoride varnish is uncertain [23]. What is common in both reviews is that primary studies (those studies from which data are extracted) are at high-risk of bias or uncertain bias. Still, the preventive use of fluoride varnishes in babies is recommended by the American Dental Association (ADA) [19, 24] and by the International Association of Pediatric Dentistry [25].

There is consensus in the literature regarding the ability of varnish to remineralize enamel lesions. Among non-restorative treatments, varnish is effective [24, 26], with better results than fluoride gel [27]. In the remineralization of initial carious lesions of deciduous teeth, fluoride varnish with 5% NaF reached the level of 65.9% (95% CI 41.2–90.7%) of inactivated lesions [28]. The remineralizing capacity of varnish was also confirmed when only studies with patients with ECC (early childhood caries) were considered [29].

The vast majority of studies are performed with Duraphat (Colgate-Palmolive), which was the first fluoride varnish manufactured in the 1960s and is considered the gold standard when it comes to research. However, variations have been observed in the formulas of fluoride varnishes, with the addition of active ingredients such as tricalcium phosphate (TCP), calcium and phosphate, calcium sodium phosphosilicate (CSPS), amorphous calcium phosphate (ACP), casein phosphopeptide and amorphous calcium phosphate (CPP-ACP), xylitol, among others [30], aiming to increase the formation of CaF2 on the tooth surface and potentialize remineralization in the body of the lesion [31], in a synergistic effect with fluorine. Varnishes with 5% NaF and CPP-ACP [32] and with TCP have already demonstrated, in randomized clinical trials, greater remineralizing potential than varnish exclusively based on 5% NaF in children with ECC.

#### **3.2 Application protocol and periodicity**

The application of fluoride varnish is indicated for babies with high-risk or caries activity; for low-risk patients who live in regions with fluoridated water, there is no need for professional fluoride application, for these patients, only brushing with fluoridated dentifrice is recommended.

The fluoride varnish application protocol has variations according to the manufacturer's instructions. Some products require humidity for application on tooth surface




#### **Table 2.**

*Commercially available fluoride varnishes, percentage of fluoride, other active components, and protocol of use according to manufacturers.*

and others recommend drying the teeth; there are different guidelines regarding the time to restrict toothbrushing and ingestion of hard food after application. Therefore, it is essential to read the instructions before using a specific product. The application characteristics of each varnish, according to the manufacturer's recommendations, is described in **Table 2**.

As for periodicity, it is believed that the preventive effect of fluoride varnish is achieved with 2 to 4 annual applications [9]. The American Dental Association (ADA) recommends the application of varnish every 3–6 months [19, 24], and the International Association of Pediatric Dentistry (IAPD) every 3 months, in patients with high-risk of caries [25].

When fluoride varnish is used for remineralization of initial enamel lesions, there is no standard protocol in the literature. There are reports of two applications (initial consultation and after 4 months), four consecutive weekly applications, applications every 3 months, and even daily applications [27]. More frequent applications can be advantageous, accelerating the inactivation of the carious lesions. Recently, an in vitro study confirmed this hypothesis: the use of fluoride varnish with CPP-ACP applied at 4-week intervals was more effective in remineralizing white spots than applications every 12 or 6 weeks [33]. This effect was observed in permanent teeth, but it can be extrapolated to primary teeth. Therefore, the protocol depends on the individual analysis of the patient, the clinical characteristics of the lesion, and the response obtained during the treatment.

#### **3.3 Toxicity of fluoride varnishes**

Even considering the high concentration of fluoride present in varnishes, the possibility of acute toxicity after ingestion of small amounts of the product is practically non-existent. The bioavailability of fluorides was studied after the application of fluoride varnishes in 5-year-old patients, concluding that, despite a transient increase in the amount of fluoride in the urine, the levels returned to normal after 48 hours and the product is safe for use in children [34]. Plasma levels are also far below from the toxic dosage [35].

The probably toxic level of fluoride corresponds to 5 mg of fluoride per kg of body weight. The approximate amount of fluoride in 1 g of fluoride varnish is 22 mg. Approximately 0.5 g of fluoride varnish is used in one application. In order to be toxic, it would be necessary that the baby ingests 4.5 g of varnish at once. Knowing that in a tube of Duraphat (Colgate-Palmolive), the varnish most used in the world, there is 10 mg of the product, the child would have to ingest half of its content at once, which will hardly happen.

The possible adverse effects of using fluoride varnish were evaluated based on 3 different clinical trials carried out with patients with ECC and whose treatment protocol included periodic applications of fluoride varnish. A total of 2.424 children aged between 0 and 5 years old were included. On average, each child received 4 treatments with fluoride varnish. No systemic alterations (nausea, vomiting, etc.) or the need for medical consultation, which could be considered as an adverse effect to the application of fluoride varnish, were reported by parents/guardians within 10 days after treatment. At the end of 3 years of follow-up, no adverse effects were related to the application of fluoride varnish [36].

#### **4. Conclusion**

Toothbrushing with fluoridated dentifrice is the main resource to prevent and treat white spot lesions in babies. In patients who are at high-risk of caries and during the active phase of the disease, the association with fluoride varnishes is indicated to accelerate the inactivation process of the lesions. Irrespective of the fluoride treatment selected, when correctly indicated and performed, home-based or professionally applied fluoride treatments are safe for babies and effective in controlling enamel carious lesions.

#### **Conflict of interest**

"The authors declare no conflict of interest".

*The Use of Fluoride for Enamel Caries Management in Infants DOI: http://dx.doi.org/10.5772/intechopen.113204*

### **Author details**

Ana Cláudia Rodrigues Chibinski\*, Anna Bárbara Maluf, Larissa Yumi Ito, Letícia Maira Wambier, Mayara Vitorino Gevert and Vitória Monteiro State University of Ponta Grossa, Paraná, Brazil

\*Address all correspondence to: anachibinski@hotmail.com; anachibinski@uepg.com

© 2023 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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[13] Chedid SJ, Tenuta M, Cury JA. O uso de fluoretos em odontopediatria fundamentado em evidências. In: Odontopediatria para o pediatra. Coutinho; Bonecker (coord). São Paulo: Ed.Atheneu; 2013.p. 419-430

[14] Kobayashi CA, Belini MR, Italiani Fde M, Pauleto AR, Araújo JJ, Tessarolli V, et al. Factors influencing fluoride ingestion from dentifrice by children. Community Dentistry and Oral Epidemiology. 2011;**39**(5):426-432

[15] Buzalaf MAR. Review of fluoride intake and appropriateness of current *The Use of Fluoride for Enamel Caries Management in Infants DOI: http://dx.doi.org/10.5772/intechopen.113204*

guidelines. Advances in Dental Research. 2018;**29**(2):157-166

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[17] Coclete GEG, Delbem ACB, Sampaio C, Danelon M, Monteiro DR, Pessan JP. Use of fluoridated dentifrices by children in Araçatuba, Brazil: Factors affecting brushing habits and amount applied on the brush. European Archives of Paediatric Dentistry. 2021;**22**(5): 979-984

[18] Chibinski AC, Wambier LM, Feltrin J, Loguercio AD, Wambier DS, Reis A. Silver diamine fluoride has efficacy in controlling caries progression in primary teeth: A systematic review and metaanalysis. Caries Research. 2017;**51**(5): 527-541

[19] Weyant RJ, Tracy SL, Anselmo TT, Beltrán-Aguilar ED, Donly KJ, Frese WA, et al. Topical fluoride for caries prevention: Executive summary of the updated clinical recommendations and supporting systematic review. Journal of the American Dental Association. 2013; **144**(11):1279-1291

[20] Giacaman R, Fernández C, Muñoz-Sandoval C, Fuentes N. Longer retention time of fluoridated varnishes enhances enamel remineralisation in vitro. Oral Health and Preventive Dentistry. 2017;**15** (6):569-573

[21] Slade GD, Bailie RS, Roberts-Thomson K, Leach AJ, Raye I, Endean C, et al. Effect of health promotion and fluoride varnish on dental caries among Australian Aboriginal children: Results from a community-randomized controlled trial. Community Dentistry and Oral Epidemiology. 2011;**39**(1):29-43 [22] Agouropoulos A, Twetman S, Pandis N, Kavvadia K, Papagiannoulis L. Cariespreventive effectiveness of fluoride varnish as adjunct to oral health promotion and supervised tooth brushing in preschool children: A double-blind randomized controlled trial. Journal of Dentistry. 2014;**42**(10): 1277-1283

[23] de Sousa FSO, Dos Santos APP, Nadanovsky P, Hujoel P, Cunha-Cruz J, de Oliveira BH. Fluoride varnish and dental caries in preschoolers: A systematic review and meta-analysis. Caries Research. 2019;**53**(5):502-513

[24] Slayton RL, Urquhart O, Araujo MW, Fontana M, Guzmán-Armstrong S, Nascimento MM, et al. Evidence-based clinical practice guideline on nonrestorative treatments for carious lesions: A report from the American Dental Association. Journal of the American Dental Association. 2018;**149** (10):837-849

[25] IAPD. Foundational Articles and Consensus Recommendations: Caries Risk Assessment and Care Pathways. 2020

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[27] Lenzi TL, Montagner AF, Soares FZ, de Oliveira RR. Are topical fluorides effective for treating incipient carious lesions?: A systematic review and metaanalysis. Journal of the American Dental Association. 2016;**147**:84-91.e81

[28] Gao SS, Zhang S, Mei ML, Lo EC, Chu CH. Caries remineralisation and arresting effect in children by professionally applied fluoride treatment - A systematic review. BMC Oral Health. 2016;**16**:12

[29] Schmoeckel J, Gorseta K, Splieth CH, Juric H. How to intervene in the caries process: Early childhood caries - A systematic review. Caries Research. 2020;**54**(2):102-112

[30] Downey D, Dennison J, Eckert GJ, Flannagan SE, Neiva GF, Yaman P, et al. Fluoride levels in unstimulated whole saliva following clinical application of different 5% NaF varnishes. Caries Research. 2018;**52**(6):431-438

[31] Godoi FA, Carlos NR, Bridi EC, Amaral FLBD, França FMG, Turssi CP, et al. Remineralizing effect of commercial fluoride varnishes on artificial enamel lesions. Brazilian Oral Research. 2019;**33**:e044

[32] Radha S, Kayalvizhi G, Adimoulame S, Prathima GS, Muthusamy K, Ezhumalai G, et al. Comparative evaluation of the remineralizing efficacy of fluoride varnish and its combination varnishes on white spot lesions in children with ECC: A randomized clinical trial. International Journal of Clinical Pediatric Dentistry. 2020;**13**(4): 311-317

[33] Abufarwa M, Noureldin A, Campbell PM, Buschang PH. How different time intervals between repeated applications of CPP-ACP fluoride varnish effect smooth surface enamel demineralization? Journal of Dentistry. 2021;**112**:103742

[34] Olympio KP, Cardoso VE, Bijella MF, Pessan JP, Delbem AC, Buzalaf MA. Urinary fluoride output in children following the use of a dual-fluoride varnish formulation. Journal of Applied Oral Science. 2009;**17**(3):179-183

[35] Ekstrand J, Koch G, Petersson LG. Plasma fluoride concentration and urinary fluoride excretion in children following application of the fluoridecontaining varnish Duraphat. Caries Research. 1980;**14**(4):185-189

[36] Garcia RI, Gregorich SE, Ramos-Gomez F, Braun PA, Wilson A, Albino J, et al. Absence of fluoride varnish-related adverse events in caries prevention trials in young children, United States. Preventing Chronic Disease. 2017;**14**:E17

#### **Chapter 5**

## Current Concepts on Caries Removal

*Urvashi Bhimjibhai Sodvadia*

#### **Abstract**

This chapter offers a comprehensive introduction of dental caries management, with a central emphasis on selective caries removal as a cornerstone of minimally invasive dentistry. Rooted in evidence-based dentistry and a grasp of carious dentin progression, the shift from conventional dental paradigms is explored. Various challenges and debates surrounding selective caries removal techniques are discussed, encompassing non-selective, selective, and stepwise methods. Histological and clinical identification methods for carious dentin are explored till the date, including color, hardness, and texture variations. It is important to pay attention to the connection between tactile examinations and the International Caries Detection and Assessment System (ICDAS) index. The relationship between clinical staging and histological aspects of carious dentin is established. Diverse techniques like hand excavation, polymer-based burs, chemomechanical agents, air abrasion, lasers, and tungsten carbide burs are evaluated, highlighting benefits, limitations, and comparisons. The chapter underscores selective caries removal's role in minimally invasive dentistry, focusing on tissue preservation and its impact on pulp vitality, restoration durability, and patient well-being. This comprehensive presentation covers clinical, histological, and technological facets of caries management in a minimally invasive context.

**Keywords:** contemporary methods, dental caries, demineralized dentin, remineralized dentin, selective caries removal

#### **1. Introduction**

Dental caries is the most common dental disease caused by an imbalance between healthy microorganisms and cariogenic species. This ecological shift is developed and maintained by frequent consumption of fermentable dietary carbohydrates. This imbalance might disrupt the demineralization and compensation cycles, resulting in net mineral loss of the tooth structure, which leads to dental cavities [1].

Dental caries cannot be completely cured by only eliminating the causative microorganisms. Instead, it can also be treated by behavioral management. It includes reduction in consumption of fermentable dietary carbohydrates and frequent disruption of bacterial biofilm from the tooth surface. Bacterial penetration cannot be prevented if such management is not being practiced by the patients which can result in irreversible inflammatory damage to the pulp [2].

Contemporary dental practice is based on evidence-based dentistry, a clear understanding of carious dentin progress, and an awareness of potential of remineralizable dentin. Therefore, a shift from G. V. Black's concept of 'extension for prevention' to minimally invasive dentistry is being employed in the clinical practice [3]. Despite the development of adhesive dentistry and spreading awareness of environmental alarms over mercury levels, the acceptance towards minimally invasive techniques for caries removal is debatable. Therefore, Selective Caries Removal in Permanent Teeth (SCRiPT) Trial is being conducted to remove the barrier of confusion on this subject [4].

This chapter emphasizes the importance of selective caries removal approach, highlighting its advantages in preserving tooth structure, pulp vitality, and longevity. Different methods of caries removal, including non-selective, selective, and stepwise techniques, are discussed, with selective removal offering favorable outcomes. Histological analysis assists in distinguishing remineralizable and healthy dentin layers that provide guidance for an accurate excavation. The use of various caries removal techniques like polymer-based burs, chemomechanical agents, air abrasion, lasers, and tungsten carbide burs are explored.

#### **2. Concepts of caries removal**

According to a consensus assessment published by Schwendicke et al. numerous approaches of caries eradication have been explained in the literature and can be classified into three types [5]:


Although selective caries removal in a single visit presents more favorable outcome than step-wise selective caries removal approach, this has been remained as a controversy among the dental practitioners [6]. This can be due to inability to compare the long-term outcome between two techniques. One explanation for the preference for one-step selective caries removal is the avoidance of iatrogenic damage to the tooth structure while re-entering the cavity in order to replace the tooth with a permanent restoration, which would have been unavoidable with stepwise caries removal [7].

The primary goal of removing carious lesion is to preserve the pulp vitality in order to increase the tooth longevity in the mouth [8].

#### *Current Concepts on Caries Removal DOI: http://dx.doi.org/10.5772/intechopen.113122*

Recent recommendation on caries removal by consensus statement by Schwendicke et al. [9]:


#### **3. What makes selective caries removal the cornerstone of minimally invasive dentistry?**

Minimally invasive dentistry focuses on preservation of soft and hard dental tissue when managing carious lesions. This technique not only encompasses removal of carious lesion to firm dentin, but it also includes conservative approach including behavioral modification. This approach comprises of diet modification, disruption of biofilm by chemical or mechanical ways, and cutting of the nutrient supply of cariogenic biofilm by sealing it hermitically [10]. When this principle is applied to the operative dentistry, it can be inferred that selective caries excavation followed by placement of a restoration with hermetic peripheral seal will preserve the dental tissue even without having complete eradication of bacteria from carious dentin.

Traditional non-selective caries removal technique can result in dentin-pulp complex damage, compromised mechanical integrity of tooth structure, and in some cases, it can also lead to irreversible damage primary odontoblasts. Conversely, contemporary selective caries removal can preserve the remineralizable dentinal structure and primary odontoblast that will help in forming tertiary or reactionary dentin [10]. By dropping the burden of cariogenic bacteria, it reduces the number of bacteria approaching pulpal tissue via dentinal tubules. By and large, it boosts the tooth life and decreases management cost and burden associated with teeth overtime [9].

It should be noted that even though the adhesion of composite resin to remineralizable dentin is weaker, it is clinically nonsignificant and cannot hamper the overall outcome of tooth restoration. This can be explained by formation of hermetic seal of resin-based adhesive to peripheral sound enamel or dentin in aptly prepared cavity [11].

The primary challenge with the selective caries removal procedure is the ability to distinguish carious dentin clinically. Sometimes, it becomes difficult to decide the extent of removal, which may impact overall long-term outcome of the restoration.

#### **4. How can we clinically distinguish between different stages of dental caries?**

Carious dentin can be identified clinically based on the color, hardness, texture and results of caries detecting dyes. Following part describes the different techniques of caries identification, and their clinical comparison based on the literature.

Among all the techniques, visual and tactile examination are the most commonly employed techniques. The primary dentinal carious lesion can be categorized into

four different types when it is examined under natural daylight or standard dental light. These four colors are yellow, light brown, dark brown, and black. Whereas, Hellyer et al. categorized hardness of dentinal carious lesion as hard, medium (leathery), and soft [12].

The hardness of non-carious dentin ranges between 51 and 65 KHN. It drops down to 6.7 KHN in presence of active lesion, whereas hardness of arrested lesion reduces to 39.2 KHN. In other words, hardness of remineralizable and healthy dentin is 30.7 and 60 KHN, respectively [13].

Clinically, the texture or hardness can be evaluated using a new Ash No. 6 prob. The hard lesions are similar to the intact dentin. When moderate pressure is employed, a fresh Ash No. 6 probe can easily penetrate medium or leathery lesions and encounters resistance during withdrawal. Although soft lesions demonstrate smooth penetration of a fresh Ash No. 6 prob. with no resistance upon its withdrawal [14, 15].

The active and inactive carious lesions show different characteristics in terms of color and texture. **Table 1** and **Figure 1** show the difference in signs of staging of carious lesions [16]. The visual and tactile examinations show high specificity but due to subjective nature of tests, they show less reproducibility, and low sensitivity [17].


#### **Table 1.**

*Outlines clinically identifiable aspects of active and inactive lesions at various stages of cavity lesion progression.*

#### **Figure 1.**

*Three distinct case situations that demonstrate active and inactive lesions depending on certain characteristics. The images on the left and center show active lesions, whereas the image on the right shows a tooth with both types of lesions.*

#### *Current Concepts on Caries Removal DOI: http://dx.doi.org/10.5772/intechopen.113122*

According to the recent systemically reviewed evidences, when we compare the visual and tactile examination, the later produces results with more reliability and specificity. Therefore, it is desirable to opt for the tactile examination over the visual inspection when performing routine inspections to identify caries. It could be stated that texture variations warrant a higher index score. This evidence is in accordance to the current recommended dental caries scoring index, the International and Caries Detection Assessment System (ICDAS) [18].

#### **5. How is the clinical categorization of stages connected to the histological characteristics of dentinal caries?**

In the literature, correlation between different color, hardness, and texture of the carious dentin to histological staging of the carious process has been described. The clinical changes of the carious lesion can be resulted from the acidic attack of the cariogenic microorganisms. This acidic dissolution can change the color, texture, and the moisture element of dentinal tissue. Based on the color and texture of the dentinal tissue, the histological staging can be discriminated [19].

To identify the clinical carious examination technique with high sensitivity and specificity, it is advisable to compare the available methods in relation to the microbial activities in form of CFU. This will enable us to identify an accurate consistent method to evaluate caries quantitatively and qualitatively [18].

There are a few research that investigated the relationship between bacterial CFU and the color or hardness of carious dentin in the literature. Orhan et al. found no significant difference in bacteria CFU and color of carious dentin, although harder lesions had considerably fewer bacteria CFU than softer dentin [20]. Comparable outcomes were noted in the Lynch et al. investigation. They discovered that soft and leathery lesions, regardless of color, had greater CFU. There was no association between the color of the lesion and the overall number of bacterial species [21].

#### **6. Which portion of the dentinal caries needs to be eliminated during the selective caries removal?**

The precise point at which caries excavation ends clinically is ambiguous and remains subjective. As a particular dentist may be influenced by their own personal experiences, diagnostic tests such as surface hardness, dentin color, or caries detector dyes, are not completely reliable. As a result, a histological analysis is the optimal tool for evaluating carious changes in the dentin and obtaining consistent results to evaluate excavation procedures.

It has been observed that the carious dentin can be divided into distinctive layers using histological staining such as outer dentin containing non-remineralizable necrotic collagen matrix and cariogenic bacteria, and inner layer composed of non-altered collagen fibers. The bacterial count is negligible in the inner layer, and the possibility of remineralization is higher [22].

Mallory-Azan staining allow to differentiate histological staging of carious dentin. Blue staining is identified as un-denatured collagen containing dentin; while altered collagen matrix will catch red staining. Thus, clinically it enables accurate identification of remineralizable or healthy dentin and demineralized dentin, respectively [23].

As previously mentioned in the chapter, hardness is a better \_\_\_\_ to evaluate the severity of carious lesion and plays an important role in determining treatment modalities as well. For instance, softer lesion needs the immediate intrusive clinical modality (e.g., debridement and restoration) as it harbors more cariogenic microorganisms as compare to the harder lesions which does not contain any cariogenic microorganisms. Therefore, harder shallow lesion does not demand any clinical interventions [14].

Selective carious removal to firm dentin is advisable for moderately deep active areas. This procedure allows leathery dentin to left behind at the center of cavity towards pulpal area, whereas caries should be removed until firm hard dentin is reached towards the peripheral dentin and marginal walls of the cavity. Clinical terminology refers to leathery dentine as having a small "tackiness" and not deforming when an object is placed against it. Hard dentine requires pushing pressure to engage, and a scratchy sound known as "cri dentinaire" can be heard [3].

#### **7. What is the recommended method for removing dental caries?**

The traditional approach of treating dental caries entails removing the remineralizable tooth tissue and replacing it with a restorative substance. Using a dental air rotor drill is currently the most popular way to remove cavities. Despite its extensive use, potential side effects include dentinal sensitivity, intense noises while using, thermal stimulation of the pulpal tissue, bone-conducted vibration, and pressure inside the tooth itself. Whereas hand excavation allows the operator to have tactile sensation while removing carious tissue. Rotary instruments greatly limit tactile feedback during selective excavation and increase the possibility of iatrogenic removal of extra tooth structure at this critical region [24]. Other techniques investigated include air abrasion, chemical agents, polymer burs, and lasers for the eradication of caries.

#### **7.1 Tungsten carbide bur**

Utilizing burs on a high-speed handpiece to reach the carious area and a low-speed handpiece to eliminate carious dentine are the most common approaches for removing caries and preparing cavities. Steel bur excavation and traditional rotary practices result in over-preparation by removing greatest amount of sound tissue possible while potentially overextending the cavity and destroying healthy tissue. Additionally, it generates pressure and heat on the pulp, vibration, noise, causes pain stimulus, and requires the need for local anesthetic. This approach leaves aversion and pain anxiety in many individuals, particularly children [25].

Studies have been conducted to compare the traditional approach with other techniques. Divya et al. investigated the time required to remove the caries and found that the traditional burs (151 sec) remove caries faster as compared to Polymer bur (344.80 sec), Papacarie (359.60 sec), and Carisolv (461.60 sec). The number of bacterial colonies after caries excavation differed considerably among the four agents utilized, with Polymer bur samples containing the highest and Stainless Steel Bur representing only 10% of the specimens [26]. Whereas Somani et al. found no significant difference in the presence of microorganisms following caries eradication between traditional burs and smart burs [27].

#### **7.2 Polymer based bur**

The fundamental objective behind developing a polymer-based bur was to create a device with a hardness of 40–50 KHN, which falls between the hardness of healthy and cariously infected dentin. As a result, they selectively remove demineralized dentin and whenever the cutting edges come into touch with either healthy or remineralizable dentin, they become blunted. **Table 2** described characteristics and specifications of available the polymer based burs and traditional carbide bur.

There was no significant difference in the outcome was observed by Usha and Ranjani when different types of polymer bur were compared [28]. Furthermore, Lohmann in et al. concluded that PolyBur P1 was only able to remove carious dentin from 33.9% of the carious teeth specimen. Additionally, the remaining denatured collagen layer was significantly thicker (more than 1 mm) than in the conventional carbide bur group [13].

#### **7.3 Chemomechanical caries removal agents (CMCR)**

Since 1975, this modality has been utilized to soften diseased dentin before carefully removing it with hand devices. Because it only affects demineralized dentin, the goal of conserving damaged dentin can be achieved [29]. They are classified into two types: sodium hypochlorite (NaOCl)-based agents (e.g. GK-101, GK-101e, Carisolv) and enzyme-based agents (Papacarie, Biosolv, Carie-Care, Brix3000).

By subsequently destroying the irreparably weakened collagen fibers in demineralized dentin, CMCR chemicals enable removal while preserving the remaining intact impacted dentin [30]. Chlorination, which entails hydrolysis of linkages between tropocollagen components and/or breaking of polypeptide chains in the triple helix, is the key technique used to do this [31]. However, little is yet known about the chemistry of amino acid chlorination and its effects. In contrast to enzyme-based CMCR agents, which rely on an additional mechanism, NaOCl-based CMCR products use the chlorination process as their main mode of action [32].

The fundamental mode of action in enzyme-based CMCR agents is papain, which is considered an effective chemical debriding agent with antibacterial and anti-inflammatory properties. It is a cysteine protease developed from the fruits and latex of green papaya (Carica papaya. The papain is believed to work by promoting the decomposition of partially deteriorated collagen strands and assisting in the deconstruction and removal of fibrin mantle created by carious process and causing no harm to unaffected collagen filaments. As a result, the demineralized dentin softens, making it possible to remove it with no anesthesia and using non-cutting instruments. An absence of −1-antitrypsin in demineralized dentin rationalizes such unique relationship [33].

A risk to dental practitioners' wellbeing exists during the COVID-19 pandemic because many dental treatments generate aerosols that could be associated with the transmission of serious respiratory illnesses. Consequently, dental and health professionals cautioned against overusing aerosol-generating therapies during the outbreak. Given the aforementioned suggestions, utilizing CMCR products that need little to no aerosol-generating techniques instead of surgery in caries prevention may be more proficient for people of all ages, especially those who suffer from anxiety or have special needs [34].

The CMCR approach is becoming more commonly recognized and comfortable for patients, as it reduces pain, anxiety, and the need for a local anesthetic. Hosein


#### **Table 2.**

*Describes the available polymer-based burs and conventional carbide burs along with their properties and specifications.*

and Hasan reported the procedural time for CMCR as 12.97 min in 27 cases, whereas convention steel bur took 7.4 min for caries removal [35]. The CMCR approach could be taken into account as a possible replacement therapy modality in the field of dental care in the future, despite its extended length and greater expense.

#### **7.4 Air abrasion**

Air abrasion, utilized as a method for removing caries, involves the non-rotary abrading of a surface by directing a stream of high-speed abrasive particles produced from compressed air. Its mechanism involves cutting at the tip and shapes shallow, saucer-like cavities with vague boundaries. In contrast to rotary drilling, air abrasion's use of extremely small particles in contact with the tooth prevents the generation of vibrational forces, resulting in enhanced comfort and reduced stress on the tooth structure. This is especially beneficial in the dentine area near the pulp, where delicate caries elimination using the slow-speed handpiece is often uncomfortable due to the resulting vibrations. In this context, employing air abrasion with a slow cutting action becomes a practical choice. The forceful expulsion of particles through the air stream also reduces the effort required by the operator [36].

Aluminum oxide abrasives are commonly used for air abrasion in dentistry, but concerns exist regarding their potential to damage healthy tooth structure and controversial safety issues [37]. An alternative option is bioactive glass (Sylc), initially designed for bone replacement. Sylc, a bioactive glass abrasive, is available for tooth polishing and has shown potential for selective cutting. However, its cutting time is significantly longer than alumina, making it less practical [38].

#### *Current Concepts on Caries Removal DOI: http://dx.doi.org/10.5772/intechopen.113122*

Neither alumina nor Sylc contains fluoride, despite evidence of fluoride-assisted remineralization in dentine.

Farooq et al. have explored fluoride-incorporated bioactive glass, discovering that reducing sodium content increases its hardness, which could be advantageous for creating bioactive glass air abrasives. Additionally, they demonstrated apatite formation in bioactive glass in Tris buffer solution, with potential implications for its abrasiveness compared to alumina in dentine air abrasion [39]. Tan et al. invented a new customized fluoridated bioactive glass particles (Na0SR) and compared its efficacy against standard aluminum oxide particles. It was concluded that Na0SR can be considered a viable abrasive alternative for alumina in air abrasion cutting because it performs similarly and has the added benefit of potentially facilitating remineralization and hydroxyfluorapatite production [40].

#### **7.5 Lasers**

The initial dental lasers (ruby, Nd:YAG, holmium-doped Ho:YAG, CO2 lasers) led to elevated pulp temperature, microcracks, and carbonization. In the mid-1990s, the safety and effectiveness of erbium-doped Er:YAG laser were explored. Proper settings and water cooling minimized thermal damage [41]. Effective caries removal requires a laser wavelength that interacts significantly with mineral, water, or both, unless ultrashort pulses cause plasma-mediated ablation.

Er:YAG and erbium: yttrium-scandium-gallium-garnet Er:YSGG lasers (λ = 2.940 μm and λ = 2.790 μm) are highly absorbed by water and Er:YSGG is also absorbed by hydroxyl ions in tooth mineral, heating and exploding tissue from the surface. For effective caries removal, the laser pulse duration should approximately match the tissue's "thermal relaxation time," avoiding excessively short or long pulse durations that result in excessive or unnecessary energy distribution within the tissue [42]. The Food and Drug Administration (FDA) granted approval for its use in these applications in May 1997. This laser functions in a pulsed manner and incorporates a water spray in its handpiece to prevent tissue dryness and the accumulation of heat. This ensures efficient absorption of energy without causing tissue carbonization or significant heat production [43].

Montedori et al. conducted a systematic review and found no studies reported presence of residual caries after using laser to remove primary caries [44]. In a study conducted by Lui et al.; patients reported limited incidence of pain in Er:YAG laser group than drill group [45].

The carbon dioxide laser lasers can function within the wavelength range of 9000 to 11,000 nm. The most prevalent variant, working at 10,600 nm, is commonly utilized for dental procedures. This laser enables surgical operations without bleeding and minimizes discomfort following soft tissue dental surgeries. Investigations demonstrated that a 9300 nm carbon dioxide laser can prompt both chemical and structural alterations in hard dental tissues [46]. Moreover, it was observed that this laser efficiently eradicates enamel and dentine. Due to its capacity to prevent enamel cavities and eliminate caries lesions, the 9300 nm carbon dioxide laser is gaining increased attention in the field of dentistry for managing dental caries [47].

For laser treatment, the increase in pulp chamber temperature should not be greater than 5.5°C. Otherwise, severe heat buildup could harm the dentine-pulp complex by disrupting odontoblasts, destroying them, or possibly causing pulp necrosis. According to a series of studies, the thermal effect of 9300 nm carbon dioxide laser

irradiation on pulp is minimal because the laser is absorbed extremely close to the surface of the hard tissues and shortly converted into heat [48]. Assa et al. also reported that the usage of water aerosol spray with laser would significantly decreases the heat accumulation.

Exposure of enamel to a 9300 nm carbon dioxide laser leads to decomposition of its constituents. This process can cause enamel to melt and fuse due to the temporary elevation in temperature, which exceeds a certain threshold. The laser modifies the enamel's microstructure and surface appearance after it cools down. Additionally, the laser induces chemical transformations in dental hard tissues, including a reduction in carbonate levels. This change transforms carbonated apatite crystals into purer forms, rendering enamel less susceptible to acid erosion. Moreover, the calcium and phosphorus content in both enamel and dentine increases following laser treatment. The dentine subjected to irradiation displays three distinct intrinsic phosphate bands under Fourier transform infrared microscopy, indicating heightened crystallinity. A study highlights that the 9300 nm carbon dioxide laser raises the calcium-phosphorus ratio in healthy enamel and dentine, thus reducing their solubility. Furthermore, the results imply that the laser treatment eliminates organic components from decayed enamel and dentine [49].

#### **8. What factors contribute to a favorable prognosis for the selective caries removal method?**

A growing database of studies has also demonstrated that the selective caries excavation technique is prognostically effective. This procedure avoids injuring the dentine-pulp complex and removing a significant quantity of tooth tissue, preserving the pulp's vitality and improving the tooth's long-term prognosis.

In the management of deep carious lesions, it's essential to thoroughly assess the final restoration to prevent potential failures in the tooth-restorative complex. Dentists should carefully inspect the restoration for surface irregularities and proper marginal integrity to avoid creating areas where plaque can accumulate. Leaving some carious tissue under a restoration after selective excavation could lead to legal issues. Patients need to be informed about the rationale behind this approach and the need for regular check-ups to ensure the tooth's pulp health over time Nevertheless, for long-term stability, cuspal coverage would be necessary to counteract the impact of repeated stress on the restoration for some cases.

#### **9. Conclusion**

In conclusion, dental caries, a prevalent condition resulting from an imbalance in oral microorganisms triggered by fermentable carbohydrates, causes mineral loss in tooth structures. Traditional treatment approaches, involving complete bacterial eradication, have shifted towards minimally invasive strategies. Selective caries removal, a pivotal aspect of minimally invasive dentistry, preserves remineralizable dentin, vital odontoblasts, and supports the formation of reactionary dentin. Techniques like polymer-based burs, chemomechanical agents, air abrasion, and lasers offer less discomfort, reduced bacterial count, and more conservative excavation. Clinical staging, utilizing color, hardness, and texture evaluations, assists in precise diagnosis and treatment planning. While challenges persist,

*Current Concepts on Caries Removal DOI: http://dx.doi.org/10.5772/intechopen.113122*

evidence supports the effectiveness of selective caries removal, enhancing tooth longevity and patient comfort.

#### **Conflict of interest**

The authors declare no conflict of interest.

#### **Author details**

Urvashi Bhimjibhai Sodvadia Nova Southeastern University, Clearwater, Florida, United States

\*Address all correspondence to: urvashisodvadiya@gmail.com

© 2023 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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#### **Chapter 6**

## Perspective Chapter: Cost-Effectiveness of Caries Preventive Programs

*Thomas Davidson*

#### **Abstract**

This chapter presents the methodology of health economic evaluations within the field of oral health and more specifically discusses the need for cost-effectiveness analyses of various caries preventive programs. It also deals with prioritized questions of limited resources and effective implementation of caries preventive technologies. As the societal cost of caries preventive interventions may not equal the direct cost of the intervention, the cost-effectiveness results depend also on the perspective and time-horizon of the analysis, and several such key issues are presented. The chapter also presents some basics about simulation modeling techniques and how to deal with uncertainty in health economic evaluations. An example of a cost-effectiveness analysis of a caries preventive program and aspects to consider is furthermore provided, to guide in the economic evaluation. Some evidences of the cost-effectiveness of various caries preventive programs are presented. However, there are some methodological weaknesses in many of the studies, and more research is needed to determine the value of such programs.

**Keywords:** economic evaluation, cost effectiveness, quality of life, QALY, willingness to pay

#### **1. Introduction**

Oral diseases have a significant impact on the global economy, resulting in a burden of over US\$700 billion, which includes both direct and indirect costs [1]. This amounts to almost 5% of the global health expenditure. Dental caries is responsible for 15% of this burden, but the actual percentage may be higher due to its association with other oral diseases [2]. Consequently, dental caries presents substantial economic challenges for individuals and healthcare systems alike.

At the same time, dental caries can result in significant health losses, from physical pain and tooth loss to psychological and financial burdens. Hence, there are many reasons to prevent dental caries.

Reducing the incidence of dental caries may be achieved through various programs. However, implementation of these programs requires investment of resources, both private and public. As resources are limited, it is essential to allocate them

judiciously. Prioritizing the allocation of resources based on criteria such as effectiveness, ethics, and cost-effectiveness is important.

Comprehensive health economic evaluations play a crucial role in prioritizing healthcare services. Although a significant portion of oral health services is privately paid, there is still a need for guidance in the cost-effective use of resources. Many countries subsidize dentistry to some extent, and it is important that public resources are used efficiently. Moreover, public dental health programs may require health economic evaluations as they involve societal investments.

Health economic evaluations provide decision-makers with valuable information to allocate resources efficiently, develop guidelines, and fund research. They can also identify potential disparities in access to preventive care and oral health outcomes across different population groups. Decision-makers can use this information to direct certain programs to vulnerable groups to improve overall oral health equity.

This chapter presents health economic evaluations, different types of analyses, and some methodological issues such as the perspective and time horizon of the analysis. It briefly explains how to calculate the costs and outcomes of a program, how cost-effectiveness should be understood and interpreted, and the need for simulation studies and uncertainty analyses. A fictive example about the cost-effectiveness of a fluoride varnishing program in school is also presented. Finally, some general findings about the cost-effectiveness of caries preventive programs are discussed.

#### **2. Health economic evaluations**

#### **2.1 Type of analyses**

When it comes to health economic evaluations, there are various types of analyses. The main difference between them is how the outcomes are handled. In general, all analyses aim to determine the opportunity cost of a particular healthcare program in relation to its additional effects. Opportunity cost refers to the benefits that resources could have yielded if spent in the most optimal way. To determine the opportunity cost of a program, it should be compared with the best alternative [3].

Commonly used health economic analyses are:


A cost analysis is a type of evaluation that focuses only on measuring the costs of a program and is not intended to be used for comparing different programs. It does not require a comparator since its purpose is only to determine the cost of a particular program.

A cost-minimization analysis, on the other hand, can be used for prioritizing between different programs. This type of analysis assumes that the consequences of the compared programs are identical. Therefore, it studies the costs of at least two programs and recommends the one with the lowest cost. Ideally, there should be evidence supporting the assumption of equal outcomes to use this analysis; otherwise, it may lead to unintentional rationing.

A cost-effectiveness analysis is used when the outcomes are expected to differ. The outcome measure in such an analysis could be any measure that is relevant to the treatment analyzed, such as life-years gained, the number of infections, prevented decayed, missing, or filled teeth (DMFT), and so on.

A cost-utility analysis is similar to a cost-effectiveness analysis, but it relates outcomes to some kind of values that represent utility or quality of life (QoL), most often quality-adjusted life-years (QALYs).

Another type of analysis is the cost-benefit analysis, which uses monetary terms for both the costs and the outcomes. This is often measured by studying the willingness to pay. If the outcomes are valued higher than the costs in such an analysis, this means that the treatment has a positive net benefit and, hence, that it should be implemented. Such monetary outcomes may be especially relevant in dental care, as people are generally used to paying most of the costs themselves, in contrast to other healthcare fields where much of the cost is often covered by society or insurance companies.

The analyses presented above can be complemented by a budget impact analysis [4], which evaluates how the introduction of a new program affects one or several budgets and what other consequences are expected for the main actors. All types of health economic analyses may be suitable for economic evaluation of caries preventive programs. Which one to use depends on what question the evaluation strives to answer.

#### **2.2 Perspective and time horizon of the evaluation**

A health economic analysis can be performed from different perspectives. The healthcare and societal perspectives are the most used perspectives. The healthcare perspective covers all costs and effects that occur within the healthcare system such as clinic costs, material used, overhead costs, and so on. On the other hand, the societal perspective includes all costs and effects on society, regardless of who they impact. This perspective also considers indirect costs such as productivity loss and the need for informal care. Other perspectives may include the third-party payer, the clinic's, or the patient's perspective. Although there is no international agreement on what perspective to use [5], the health care and societal perspectives are the two most common in various national guidelines. Generally, the perspective used depends on the analysis's aim to provide information.

Most methodological guidelines in health economic evaluations agree that the analysis of costs and effects should have a time horizon that is long enough to reflect all significant differences in costs or outcomes related to the assessed program. This means that evaluations in, for example, prosthetics should include all future costs and effects related to the treatment, which may be a lifetime. The same applies to caries preventive programs, as this may affect the future incidence of new caries. However, the time horizon of the analysis should also be based on the level of data that exists and the uncertainty around that data. For instance, a decision-maker who considers a caries preventive program at school may not find a lifetime horizon to be relevant or trustworthy. Instead, a time horizon of, for example, 5 years could be more relevant.

It is essential to discount costs and effects that occur in the future annually to reflect their values at the time of the analysis. Although the discount rates may vary between guidelines, a rate of 3% per year is commonly used.

#### **2.3 Costs**

A program's cost goes beyond its price tag. The costs incurred are determined by the resources utilized and should be evaluated based on what those resources could have been used for elsewhere (the opportunity cost). It is best if all resources used are quantified and presented in natural units before being valued, to ensure transparency and transferability to other settings.

There are various ways to classify costs, but they are usually categorized as direct and indirect costs, or initial treatment and maintenance costs. Direct costs refer to resources such as dental personnel, all supplies and material used, dental clinic, administrative, and patient costs. Indirect costs are productivity losses linked to a treatment or health condition. Maintenance costs include all treatment costs that are not part of the initial treatment.

To accurately reflect the direct costs, the number of minutes the dental team spends on patients should be measured and valued. If a societal perspective is sought, all the time used by patients (and significant others, if applicable) should also be included. If the program requires multiple visits to the dental clinic, all time spent needs to be added up. The value of patients' time may be difficult to estimate, but it should reflect their opportunity cost. If the time used by patients would have otherwise been spent on paid production, the cost of having a person employed should be used (using the human capital method). If time off is used, the opportunity cost of this time off should be used, sometimes valued at 30% of paid production.

#### **2.4 Outcomes**

There are three different types of outcome measures that can be used to evaluate programs: clinical outcomes (including intermediate outcomes), measures of quality of life (QoL), and monetary outcomes. The choice of which measure to use depends on the purpose of the analysis.

Health economic evaluations allow for any kind of outcome measure to be used, but it is important that the chosen measure is relevant to the program being evaluated. For example, when evaluating a caries preventive program, the outcome measure should reflect the aims of the program. This might include factors such as the amount of caries, dmft/DMFT, pain, esthetics, or QoL.

The most commonly used outcome measure in healthcare programs is quality adjusted life year (QALY). This measure combines the value of a health state with the time spent in that state, providing scores on a scale with common anchor points. However, if QALY is to be used in caries preventive programs, it is important to consider whether it can capture oral health-related quality of life (OHRQoL) aspects [6].

It is necessary to choose outcome measures carefully, as they are used to inform decision-makers about the cost-effectiveness of proposed programs. If the chosen measure is not relevant to the decision-maker, then the analysis will not be useful. Therefore, it is important to consider the purpose of the analysis and who the decision-maker is when choosing an outcome measure.

#### **3. Cost-effectiveness**

If the evaluation's focus is on determining the cost-effectiveness of a program, then a cost-effectiveness analysis or cost-utility analysis is generally required. In this type of analysis, an incremental cost-effectiveness ratio (ICER) is calculated by dividing the difference in costs between at least two alternative programs with the difference in effects between the programs.

For instance, if program A costs \$100 per participant and causes 1 DMFT and program B costs \$50 per participant and causes 1.5 DMFT, this means that program A costs an additional \$50 and prevents 0.5 DMFT compared to program B. The ICER would then show that it costs an additional \$100 per DMFT prevented. Whether this is cost-effective or not depends on the willingness to pay to prevent DMFT. However, no such willingness-to-pay value exists, which makes it difficult to state if program A is cost-effective.

In the case where the outcome measure instead is QALY, the same program A may yield 0.80 QALY and program B 0.79 QALY, resulting in an increase of 0.01 QALY. The ICER of program A compared with program B would then be \$5000 per QALY gained (as shown in Eq. (1) below). Since QALY has been used in many studies and previous decisions, we know that the willingness to pay per QALY gained use to be much higher than \$5000. Therefore, program A can be considered cost-effective compared with program B.

Eq. (1). Calculation of the incremental cost-effectiveness ratio (ICER) of program a compared with program B is as shown below:

$$ICER = \frac{CostA - CostsB}{EffectsA - EffectsB} = \frac{\\$100 - \\$50}{0.80 \, QALY - 0.79 \, QALY} = \frac{\\$5000}{QALY} \tag{1}$$

A cost-effectiveness analysis can be represented on a cost-effectiveness plane, as shown in **Figure 1** below. The ICER can be located in any of the four quadrants, A, B, C, or D. Only in quadrant A or D, it is straightforward to determine whether the program being evaluated is cost-effective, as it leads to either increased costs and lower effects or decreased costs and higher effects. However, in quadrant B or C, it depends

#### **Figure 1.**

*The cost effectiveness plane. = ICER of program a compared with program B.*

on how much one is willing to pay for improved effects. The maximum willingness to pay for an additional effect can be shown on an acceptance curve, and all estimates below this curve are considered cost-effective. The star in the figure represents the potential ICER of program A compared with program B in Eq. (1) above, indicating that program A is deemed cost-effective.

#### **4. Simulation and uncertainty**

The main purpose of health economic analyses is to assist decision-makers in prioritizing between different programs or treatments. It is crucial to provide relevant and reliable information for this purpose, which often requires the use of simulation models and uncertainty analyses. In this part, we will briefly introduce these concepts.

#### **4.1 Simulation models**

Simulation involves creating models that replicate real-world scenarios to assess the potential impact of different programs on costs and outcomes. These models can be used to estimate the long-term costs and effects of a program and to synthesize available data from different sources [7]. Simulation models can also be used to generalize results from one context to another and to go from intermediate outcomes to other outcomes. There are three main types of simulation models used in health economic evaluations: decision trees, Markov models, and discrete event simulation.

Decision trees are the simplest type of model and are useful when the timeframe is short and when the process is not complex. For instance, a decision tree may be relevant in a caries preventive program to model how many people received caries with different programs. However, it is not suitable if you want to follow the caried tooth for a long time.

A Markov model is built around different states, in which a patient can stay or transfer to another state with certain probabilities. Such a model can be useful for dental caries as it may have a state of a sound tooth, another state with enamel caries, another one with dental caries, and so on. The model can run for many cycles, and the tooth (or the patient) may move between these states, causing costs or effects continuously, which can be summed up.

Finally, discrete event simulation is generally more advanced and requires data on a patient level. It allows the study of systems or processes whose state changes discretely over time.

#### **4.2 Uncertainty**

Uncertainty is a common challenge in health economic evaluations due to factors such as variations in patient outcomes, differences in care settings, and uncertainty in cost estimates. To ensure the reliability and application of evaluation results, it is important to address and quantify uncertainty. Sensitivity analysis is a fundamental tool for assessing the impact of uncertain parameters on the results of economic evaluations.

Sensitivity analysis involves varying key inputs and assumptions, such as treatment costs, disease prevalence, and treatment effectiveness, to identify the variables that have the greatest impact on outcomes. This process allows researchers to understand the strength of the results and make better-informed decisions.

#### *Perspective Chapter: Cost-Effectiveness of Caries Preventive Programs DOI: http://dx.doi.org/10.5772/intechopen.113817*

There are different types of uncertainty that need to be dealt with differently. Parameter uncertainty relates to the fact that the true value is unknown, and this can be tested with statistical tests. Methodological uncertainty relates to all the methodological choices that must be made in an economic evaluation, and the significance of this can be tested by varying those choices. Structural uncertainty relates to how the decision is labeled, the comparator chosen, and the simulation model created. This type of uncertainty is often the most difficult to test.

Another type of sensitivity analysis is called bootstrapping, which is a statistical resampling technique used to estimate the uncertainty around cost and outcomes. It involves drawing multiple random samples with replacement from the original data to generate a distribution of cost and effectiveness estimates. This method provides confidence intervals around the point estimates, which help to understand the level of certainty in evaluation results.

Probabilistic sensitivity analysis goes further than traditional sensitivity analysis by including probability distributions for uncertain parameters. Instead of using single point estimates, a probabilistic sensitivity analysis assigns probability distributions to these parameters and performs thousands of simulation runs to generate a range of possible outcomes with associated probabilities.

In summary, simulation techniques and uncertainty analysis are important components of health economic evaluations. By using sophisticated simulation techniques and carefully addressing uncertainty, researchers can increase the credibility and utility of their findings. This can guide decision-makers in making evidence-based decisions that improve oral health outcomes and optimize resource allocation in dental care systems.

#### **5. An example: fluoride varnishing program in school**

Here follows an example of an economic evaluation of a caries preventive program and some aspects to consider. In this example, we evaluate a fictitious caries preventive program from a health economic perspective. A decision-maker is considering starting a fluoride varnishing program in schools for 12-year-old children. The main reason for this is that dental health is uneven, and high-risk children are difficult to reach. A program within schools has a high chance of capturing these hard-to-reach children. The comparator is no such program (treatment as usual).

We have chosen a cost-effectiveness analysis with the number of DMFT as the outcome measure. We have chosen a societal perspective and a time horizon of 5 years to capture the wide effects over a long time. The direct cost of each application has been found to be relatively low as a whole class can be treated within a short time. The cost includes the varnish used, the time used by dental personnel, and the costs for the person to get to the school. The cost of the involved children's time is set to zero as only a few minutes of school time are used.

Assuming the program would cost \$50 per involved child and the varnish would lead to fewer DMFT compared to treatment as usual for children with moderate or high risk of caries, we need to study the relation between increased costs and improved outcomes to examine the cost-effectiveness. For children with low risk, the program only reduces DMFT by 0.1 compared to treatment as usual. For children with moderate or high risk, the number of DMFT is reduced by 0.4 and 1.0, respectively; see **Table 1**. This reduction in DMFT furthermore leads to reduced costs; that is why the cost difference in **Table 1** is lower than the cost of the program. However, the


#### **Table 1.**

*Presentation of the incremental cost-effectiveness ratio of the example.*

difference in cost is above zero for all risk groups, which means that the program will not lead to savings.

From **Table 1** we can see that the most favorable ICER is achieved for the group with a high risk of caries, but we could not state whether this is cost-effective or not as we do not know the willingness to pay per DMFT prevented.

If all the ICER of the risk groups from the example are plotted in the cost effectiveness plane, we might more easily see the difference between the groups; see **Figure 2**. If the maximum willingness-to-pay per DMFT prevented would be \$30, the program would only be cost-effective if directed toward the high caries risk group. If the maximum willingness-to-pay per DMFT prevented instead would be \$100, the program would be considered cost-effective also if directed toward those with a moderate risk. The program is probably not cost-effective if directed toward those with a low risk of caries. However, a school-based program would probably include all these groups, perhaps showing an average in risks close to the moderate risk group.

#### **Figure 2.**

*Illustration of the cost-effectiveness of the example in a cost-effectiveness plane. = ICER of program a compared with treatment as usual for three different risk groups.*

#### **6. Evidence of cost-effectiveness of caries preventive programs**

There's plenty of evidence to suggest that caries preventive programs are costeffective, as shown by various studies and health economic evaluations over the years; for example [8, 9]. However, there are some methodological weaknesses in many of the studies, and more research is needed to determine the value of such programs [10].

Most of the studies have focused on children with a high risk of caries, with fluoride varnish being the most common intervention. While these interventions have been successful and cost-effective, they are not directly applicable to the general population where caries incidence may be lower [8].

There are a few interventions that have been found to be effective and costefficient for the general population, such as risk-based interventions [11], frequency of dental check-ups [12], and taxes on sugar [13]. However, there is a lack of studies using QALY as the outcome measured, which makes it challenging for decisionmakers to prioritize caries preventive interventions.

Water fluoridation is probably the most efficient program, which has been found to save costs and reduce caries [14]. However, many countries or regions have made this program illegal for different reasons.

Additionally, it is important to note that the cost-effectiveness of caries preventive programs can vary depending on several factors, including the target population, the specific intervention, the dental care setting, and regional healthcare costs. Therefore, conducting local or regional health economic evaluations is necessary to tailor preventive programs to the specific context and population. Overall, more research is needed to determine the cost-effectiveness of caries preventive programs and to improve their quality.

#### **7. Discussion and conclusions**

The efficient use of scarce resources is crucial in dentistry, which is why health economic evaluations are necessary. In this chapter, we have discussed various methods to achieve this objective. Health economic evaluations can also help prevent socioeconomic inequalities in dental health and support research in preventive dental care. We have also discussed the different types of health economic evaluations and identified cost-effectiveness analysis, including cost-utility analysis, as the most used ones. However, before selecting a particular methodology, it is crucial to define the decision problem clearly. This involves identifying the patients, intervention, comparator, and outcomes (PICO), as well as determining the perspective of the analysis and the cost-effectiveness threshold.

To ensure that the analysis covers all the critical aspects, it is recommended to use checklists [3]. Nevertheless, these checklists should be used in the context of their influence on the decision-making process, rather than the number of boxes ticked. Some items in the checklist may not be relevant to the decision problem, while other important aspects may be overlooked. For instance, the analysis's relevance to the decision problem, the inclusion of the best comparator, and the selection of the most relevant outcome measure are critical considerations.

It is essential to assess values that are important to patients when conducting health economic evaluations. The social function of esthetics, such as "kissing," may be more important to patients than the appearance of their teeth. Similarly, patients may view the chewing function as "meal joy" rather than merely a dental function [15].

To provide relevant health economic evaluations in dentistry, well-developed simulation models are also necessary. These models should analyze programs over an appropriate time horizon, combine sources from different areas, and explore total uncertainty. Additionally, health economic evaluations in dentistry would benefit from estimating QALY values for various dental health states.

In conclusion, assessing the cost-effectiveness of caries preventive programs is crucial in evidence-based decision-making in oral health. These evaluations provide valuable insights into the financial feasibility, value for money, and the long-term impact of preventive interventions. By understanding the cost-effectiveness of different programs, policymakers and dental professionals can develop sustainable, efficient, and equitable strategies to combat dental caries effectively and promote better oral health outcomes for populations.

#### **Conflict of interest**

The author declares no conflict of interest.

#### **Author details**

Thomas Davidson Linköping University, Linköping, Sweden

\*Address all correspondence to: thomas.davidson@liu.se

© 2023 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Perspective Chapter: Cost-Effectiveness of Caries Preventive Programs DOI: http://dx.doi.org/10.5772/intechopen.113817*

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### *Edited by Ana Cláudia Rodrigues Chibinski*

This book is the result of collaborative efforts from esteemed professors and researchers worldwide, who understand that dentistry is a dynamic field and that research on dental caries is constantly evolving. *Dental Caries Perspectives - A Collection of Thoughtful Essays* is a collection of varied insights that will enrich readers' understanding of dental caries and inspire further exploration and discussion within the field. It addresses the importance of meticulous diagnosis of caries, highlighting new methods and technologies available to clinicians, as well as the significance of adopting minimally invasive dentistry approaches, such as selective caries removal and remineralization of incipient lesions, to enhance treatment quality and preserve dental tissue. *Dental Caries Perspectives - A Collection of Thoughtful Essays* is a valuable resource for clinicians, researchers, and students, fostering critical thinking and encouraging readers to continuously seek out new knowledge and innovative solutions.

### *Sergio Alexandre Gehrke, Dentistry Series Editor*

Published in London, UK © 2024 IntechOpen © Gile68 / iStock

Dental Caries Perspectives - A Collection of Thoughtful Essays

IntechOpen Series

Dentistry, Volume 16

Dental Caries Perspectives

A Collection of Thoughtful Essays

*Edited by Ana Cláudia Rodrigues Chibinski*