## 7 Application of AI Artificial Intelligence in Cone Beam Tomography
Visualization of the effects of orthodontic treatment, including facial appearance and age-related changes, is also an interesting application of AI. The potential of robotic technology for dental and maxillofacial surgery should also be mentioned [91, 103]. Moreover, AI can be used in the diagnosis of osteoarthritis of the temporomandibular joint, to assess pathology in the mandibular conde, or to detect mycosis, inflammation, and other changes in the paranasal sinuses [104, 105, 106]. In a study conducted by [PERSON] et al. [107], the effectiveness of AI in automatically detecting and segmenting non-obstructive mesial buccal 2 (MB2) canals on endodontically obstructive maxillary molars in cone-beam tomography images was assessed. Great potential of AI in this area has been found, but it is limited by factors such as the presence of metallic artifacts or differences in canal calcifications. In an article by [PERSON] et al. [108], the diagnostic performance of an AI program based on the convolutional neural network method for the morphological classification of sella turcica (saddle turcica) on CBCT images was evaluated. The obtained results confirm the effectiveness and speed of AI in detecting anatomical landmarks, which allows for significant time savings compared to manual analysis. In the work of [PERSON] et al. [109], they evaluated the effectiveness of AI in marking teeth and dentulous areas in CBCT images. The obtained results indicate that the median time for indicating the desired elements by AI was 1.5 s and by humans 98 s, which clearly indicates the advantage of artificial intelligence in this respect. In turn, the article by [PERSON] et al. [110] confirmed the effectiveness and speed of the AI program in assessing the volume of the airway at the pharyngeal level by assessing 306 CBCT images. In another study, [PERSON] et al. [111] assessed the usefulness of AI in segmenting teeth depending on the type of filling on 74 CBCT images. Satisfactory results were obtained, indicating high accuracy and effectiveness of AI regardless of the material filling the tooth, which may directly facilitate the work of dentists in assessing tomography results. [PERSON] et al. [112] checked the usefulness of AI in segmenting the temporomandibular joint and mandibular fossa on CBCT images. The results indicated a much shorter time needed for analysis in the case of AI than when measurements were made by radiologists. Therefore, AI can help in the rapid evaluation of data for TMJ reconstruction. In another study, [PERSON] et al. [106] presented a promising concept of using AI and AR (augmented reality) for planning the implantation of dental implants in 3D technology. In an article by [PERSON] et al. [89], they used deep learning models to segment and assess the volume and structure of the masseter muscle based on CBCT images. The study showed that this method allows for similar performance to the evaluation of classic CT images by experts and significantly exceeds the efficiency of evaluation of CBCT images by the human eye, while reducing the work time by 322 times. In the last cited study by [PERSON] et al. [113], AI was used to assess the thickness of the palatal bones and soft tissues of the palate in order to select an appropriate place for the installation of an orthodontic mini-implant. As expected, the measurement results obtained using artificial intelligence were consistent with the actual situation and precise, thanks to which this technology has the potential to qualify the patient and prepare dentists for the procedure of implanting palatal mini-implants. Based on the examples cited, it can be concluded that the use of AI combined with cone tomography in dentistry and orthodontics has great potential to improve the work of clinicians and guarantee the highest quality of diagnostics. Automatic measurement and analysis of cone tomography images by AI allows, above all, to save time and effort for radiologists and dentists while guaranteeing the reliability of the results.