PROCESSING OF CBCT DATA WITH ARTIFICIAL INTELLIGENCE IN THE DIAGNOSIS OF CARIES AND ITS COMPLICATIONS

Abstract

Over the past few years, artificial intelligence (AI) technologies have been actively used in many areas of medicine, including dentistry. 
 The aim of the study is to determine the diagnostic value of IS in the detection of caries and its complications according to cone beam computed tomography (CBCT) data in comparison with clinical examination. 
 Materials and methods. CBCT images of 15 patients with carious and periodontal lesions were analyzed by an experienced dentist, who also specializes in radiology, and the Diagnocat AI software. The dentist also performed a visual examination of these patients. 
 Results. Most of all contact caries were determined using AI (n = 20), and occlusal caries − during clinical examination (n = 10). The greatest number of periapical changes was also detected using IS (n = 22). The difference between the indicators of detection of pathological foci in the assessment of IS and the radiologist was statistically insignificant, which indicates the equivalence of these methods. X-ray image evaluation revealed more contact caries compared to clinical examination (14 vs. 7, p < 0.05), but clinical examination was superior in detecting occlusal caries (10 vs. 2, p < 0.03). Periodontal disease was more accurately diagnosed by X-ray (17 vs. 9, p < 0.05). The average time for evaluation of CBCT images by a radiologist was 21.54 ± 4.4 minutes, and the AI completed the report in 4.6 ± 4.4 minutes from the moment the loading of CBCT was completed (p < 0.01). 
 Conclusion. The use of AI technologies in the analysis of CBCT images can improve the accuracy of diagnosing caries and its complications by up to 98%, as well as significantly speed up the time for making a diagnostic decision.

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