Abstract
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. 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. These modules use CNN based on state-of-the-art architectures. In total, 1346 CBCT scans were used to train the modules. After annotation and model development, the AI system was tested for diagnostic capabilities of the Diagnocat AI system. 24 dentists participated in the clinical evaluation of the system. 30 CBCT scans were examined by two groups of dentists, where one group was aided by Diagnocat and the other was unaided. The results for the overall sensitivity and specificity for aided and unaided groups were calculated as an aggregate of all conditions. The sensitivity values for aided and unaided groups were 0.8537 and 0.7672 while specificity was 0.9672 and 0.9616 respectively. There was a statistically significant difference between the groups (p = 0.032). This study showed that the proposed AI system significantly improved the diagnostic capabilities of dentists.
Highlights
In this study, a novel Artificial intelligence (AI) system based on deep learning methods was evaluated to determine its real-time performance of Cone-beam computed tomography (CBCT) imaging diagnosis of anatomical landmarks, pathologies, clinical effectiveness, and safety when used by dentists in a clinical setting
30 CBCT scans were examined by two groups of dentists, where one group was aided by Diagnocat and the other was unaided
Previous studies in dentistry focused on image-processing algorithms to achieve high-accuracy classification and segmentation in dental radiographs
Summary
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. CBCT has been shown to significantly increase the detection rate of tooth root canal spaces and periapical areas for the evaluation of dental infection and pathology compared to conventional imaging[15,16,17]. This suggests that CBCT enhances the recognition of periapical bone lesions and offers improved diagnostic accuracy, treatment planning, and prognostic outcomes. Computer-aided systems have been developed to assist in medical and dental imaging d iagnosis[24,25,26,27]
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