**1. Introduction**

Obstructive sleep apnea syndrome (OSAS) is a common sleep disorder with arising prevalence. The course of the disease is followed by repeating breath interruptions during sleep. The reason is the collapse of soft upper airway tissue. This restriction of ventilation results in several breathing difficulties, such as snoring during sleep and hypoxemia. OSAS also leads to long-term changes in autonomous functions, hypertension, or reduced left ventricular function. As a result of the propagation and activation of inflammatory pathways, immune regulation is often disrupted in pediatric patients. Early diagnostics and relevant treatment are the keys for improve the health status of patients.

Polysomnography (PSG) is the golden standard for OSAS diagnostics [1]. PSG is performed for whole night in specialized sleep laboratories and the PSG device is continuously monitoring selected vital functions, such as ECG, EEG, thoracoabdominal movements, nasal airflow, blood oxygen saturation, snoring, or body position. OSAS is confirmed if the number of apnea episodes is higher than 15 during a night and the episode takes more than 10 seconds [1, 2]. The apnea-hypopnea index (AHI) is calculated, which is used to indicate the severity of the disease.

Diagnostics and care of patients with obstructive sleep apnea vary by country and depend on the patient's symptoms. Available data suggest that most cases remain undiagnosed and untreated even in developed countries, which can increase the risk of cardiovascular, metabolic, and neural diseases and affect the quality of life. Generally, PSG is a time-consuming procedure carried out using specialized equipment, so there will be always a patient limit for undergoing the diagnostic test. As an example, there is only one specialized child and adolescent sleep laboratory in Slovakia for approximately one million children under 19 years.

Therefore, there is a reasoned demand for alternative or pre-diagnostic testing that will distinguish the patients with a high risk of OSA. Today's PSG testing can be also proved by telemedicine. Although it holds great promise to change health care delivery, it has not been proven to have the same accuracy as conventional PSG. Besides the conventional PSG, there are several supplementary testing methods. One of the best known is the sleep questionnaire [3–5] focused on the medical history of the patient and the physical examination. Another supportive diagnostic tool is the pulse oximetry or examination of a specific protein in blood serum. Mentioned questionnaires evaluate the subjective and objective symptoms, as well as the craniofacial and intraoral anatomy. These structures are considered an important indicator of the predisposition of the OSAS [6]. Nowadays there is an effort to make diagnostics more available, therefore, the emphasis is placed on the use of fast imaging techniques.

Many recent publications [7–9] focus on the face anatomy (craniofacial anthropometry, structure of the soft tissue in oral cavity, and anterior neck subcutaneous fat tissue thickness) and use advanced imaging techniques such as X-ray [10], MRI [11], and CT [12, 13]. Although, mentioned modalities do not match the criteria for cost reduction, faster procedure, and simplification of the clinical examination. Last but not least, the speed of scanning process is very important to avoid motion artifacts in resulting 3D models, especially if the system is dedicated to pediatric medicine.

As an alternative, we present an optical depth multi-sensor system that can be used excluding other emerging disadvantages with lower quality of an output model. Optical depth sensors allow capturing the nature of craniofacial anatomy needed for prediction of OSAS, such as shape and contour in a faster, cheaper, and more readily available way, compared with the other imaging techniques [14]. The geometrical precision of an output model is the key attribute for the desired application. It is also the goal of the proposed multi-camera parallel scanning system to reconstruct a complete 3D model of the object from a collection of images taken from known camera viewpoints. Therefore, it is important to choose a suitable optical sensor with the least measurement error. For a real application, the main requirement is to obtain a complete 3D model without any noisy artifacts. In this work, we aim to evaluate each of the camera technologies: The Intel® RealSense™ Depth Camera D415 Series sensors, Stereolab's ZED Mini depth camera, Microsoft Kinect for Windows V2, and Intel® RealSense™ Camera SR300 and offer a comparison of individual operating technologies. With the selection of a suitable optical sensor, also the fact that the scanning object is a pediatric patient is taken into account.

### *Usage of RGB-D Multi-Sensor Imaging System for Medical Applications DOI: http://dx.doi.org/10.5772/intechopen.106567*

Based on accuracy measurements, we prefer active stereo pair technology. The design, including the optimal topology of used cameras, user interface, and implementation of conventional OSAS screening questionnaire is introduced. Our effort is to predict the probability of OSAS occurrence without the need for traditional polysomnography testing. For this purpose, in the first stage of research, we use the scanning system primarily to obtain the 3D models of the patient's head, and subsequently, the database of point cloud models will be created for further research (automated extraction of key points in the face and head and automated measurement of geometric dependencies indicating the risk of OSAS). Currently, the absence of the database of 3D scans is the crucial limitation of OSAS data processing and assessment. Many studies dedicated to automated diagnostics of OSAS suffer from the datasets with small numbers of images and models. For this reason, building a huge datasets of 3D scans taken from various points of view with additive information about the patient is one of the main objectives of our research.

Additive information is a de facto electronic version of an internationally standardized sleep questionnaire. This dataset will be used for further automated diagnostics and research in this field. The result of our work is the system that consists of a fixing stand (that allows changing the camera layout) and software web-based application (includes the data annotation and the assistance support system) that helps the operator to set the patient's head into the normalized position. Using the advantage of machine learning it seems to be possible to evaluate the presence of OSAS from the point cloud representation of the patient's head and neck [15]. In the future, we assume the use of obtained dataset (composed of different views and facial expressions) with additive information in OSAS automated diagnostics. The experimental system is located in Martin University Hospital in Slovakia, Clinic of Children and Adolescents.
