Introduction: Artificial intelligence in recent years represents a significant advancement in the field of medical diagnostics, particularly in the analysis of three-dimensional CBCT images in dentistry. Its use improves the accuracy, speed and reliability of diagnostic procedures and reduces the impact of subjective interpretation of radiological images. Purpose: The purpose of the diploma thesis was, through a systematic review of scientific literature, to examine the use of artificial intelligence in the analysis of CBCT images in dental diagnostics, especially in the segmentation of anatomical structures, detection of pathological changes, and improvement of radiological image quality. Methods: We used the method of a systematic review of scientific literature. Relevant sources were searched in selected databases based on predefined keywords and inclusion criteria, and studies addressing the use of artificial intelligence in CBCT diagnostics were analysed. Results: The results show that artificial intelligence enables a high level of accuracy in the segmentation of anatomical structures and successfully detects pathological changes, such as caries and periapical lesions. In addition, it contributes to improving the quality of CBCT images by reducing artifacts and enhancing visualization. Discussion and conclusion: The use of artificial intelligence enables faster, more standardized, and reproducible analysis of radiological images and represents an important supportive tool in clinical decision-making. Despite promising results, its broader use in clinical practice still requires additional validation, standardization, and proper integration into the clinical environment. Artificial intelligence has great potential for further development and wider application in dental diagnostics.
|
