![]() ![]() The results for the overall sensitivity and specificity for aided and unaided groups were calculated as an aggregate of all conditions. 30 CBCT scans were examined by two groups of dentists, where one group was aided by Diagnocat and the other was unaided. 24 dentists participated in the clinical evaluation of the system. After annotation and model development, the AI system was tested for diagnostic capabilities of the Diagnocat AI system. In total, 1346 CBCT scans were used to train the modules. ![]() These modules use CNN based on state-of-the-art architectures. The system consists of 5 modules: ROI-localization-module (segmentation of teeth and jaws), tooth-localization and numeration-module, periodontitis-module, caries-localization-module, and periapical-lesion-localization-module. In this study, a novel AI system based on deep learning methods was evaluated to determine its real-time performance of CBCT imaging diagnosis of anatomical landmarks, pathologies, clinical effectiveness, and safety when used by dentists in a clinical setting. ![]()
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