**3. Computer-assisted surgery**

CAS is a new surgical concept that refers to the use of computer technology for presurgical planning and to guide or assist surgical procedures. CAS is generally considered to include (i) creation of virtual images of patients; (ii) analysis and in-depth processing of patient images; (iii) diagnosis, presurgical planning, and simulation of surgical steps; (iv) surgical navigation; and (v) robotic surgery. With its development and use in the medical field, CAS has helped realize precision surgery.

#### **3.1 Digital three-dimensional reconstruction and simulation surgery**

The technical basis of digital three-dimensional (3D) reconstruction is to convert two-dimensional (2D) cross-sectional images such as CT or MRI into 3D visual images using computer algorithms to provide the operator with more intuitive stereoscopic images for diagnosis and preoperative evaluation. Through the virtual reality surgeries available through modern computer technology, a virtual surgery model for specific individualized surgical modality evaluations can be established. The surgeon can input the conceived surgical plan into the computer, combine it with the presurgical medical images, and form a three-dimensional image after processing by the software system to understand in detail the specific location, involvement range, and adjacent relationships of the tumor, especially the involvement of blood vessels. Medical image data and virtual surgery systems are also used to reasonably customize *Application of Computer-Assisted Surgery System Based on Artificial Intelligence in Pediatric… DOI: http://dx.doi.org/10.5772/intechopen.111509*

individualized surgical plans to reduce surgical injury, avoid damage to surrounding tissues, improve the precision of lesion localization, and increase the success rate of surgery [7].

### **3.2 Research and development of CAS based on artificial intelligence**

The Hisense Computer-assisted Surgery System (Hisense CAS) was developed by Prof. Dong's group in 2013. This system can perform medical image preprocessing with CT imaging DICOM data, especially low-quality data that can be enhanced in high definition before preprocessing. The medical images labeled with features such as greyscale and texture features are then subjected to deep machine learning by U-Net on a large number of standard DICOM files. Thus, automatic and accurate segmentation of new input data is achieved. The segmentation results are processed by filtering, CT interlayer adaptive correspondence point interpolation, morphology, pattern recognition, and other algorithms, and a self-learning topological model is established to model and track the vessel shape in the three phases (arterial phase, venous phase, and balance phase) of imaging. Then, the matching cubes and ray cast algorithms are used for color rendering, and finally, the 3D alignment algorithm is used to stereoscopically align the three-phase data to accurately obtain enhanced 3D images visualizing the target organs, lesions, and blood vessels. This system can precisely observe the relationships of the lesion with blood vessels and organs in 3D, calculate the volume of the organs, lesions, and blood supply area of each blood vessel branch, perform virtual surgical resection, and determine the best surgical resection line.

With the progression of clinical needs, artificial intelligence technology, and machine learning based on big data, Hisense CAS has been updated to version 5.0. The improved algorithm enables less manual operation and a 25–30% reduction in 3D reconstruction time, and the whole process takes approximately 20 minutes. Hisense CAS can reconstruct more than 4 levels of vessels and distinguish tumors and vessels with 0.5 cm spacing. The Dice value of solid tissues can reach more than 95%, and that of ducts can reach more than 90%. Hisense CAS can also display the overall 3D anatomical relationship and pipeline variations in a semitransparent and interactive way, calculate the distance between any two points and the angle of travel of any blood vessel, the range of innervation or drainage, and the volume of organs and tumors, and provide other information that cannot be obtained from traditional 2D images. In addition, a cloud-based 3D visualization platform for precision surgery based on B/S architecture was constructed to realize the data interactions between the PC terminal browser and CAS and to store, manage and share 2D and 3D image data (**Figure 1**) [8, 9].

### **3.3 Gesture control intelligent display module (Hisense SID)**

Real-time surgical navigation is used to accurately correspond the preoperative image with intraoperative organ anatomy, and through instruments or signal transmission, real-time feedback to the image is provided to reconstruct the model, enable clear positioning, and achieve precision surgery. The 3D image gesture control intelligent display module (Hisense SID) developed by Prof. Dong's group, based on somatosensory interaction, motion capture, and other technologies, can quickly and precisely realize human-computer interactions through simple gesture operations within a specific range to ensure intraoperative sterility. The operator can rotate,

#### **Figure 1.**

*Hisense computer-assisted surgery system based on artificial intelligence.*

**Figure 2.**

*Real-time surgical navigation through Hisense SID gesture intelligent control.*

zoom in and zoom out on the digital 3D image through different gesture control commands to view the required details. In addition, the system provides intelligent tracking of specific operators, thus eliminating interference from surrounding personnel, reducing misuse, and improving recognition rates (**Figure 2**) [10].

*Application of Computer-Assisted Surgery System Based on Artificial Intelligence in Pediatric… DOI: http://dx.doi.org/10.5772/intechopen.111509*
