Evaluation of Dental Plaque Area with Artificial Intelligence Model

Abstract Objectives This study aims to assess the diagnostic accuracy of an artificial intelligence (AI) system employing deep learning for identifying dental plaque, utilizing a dataset comprising photographs of permanent teeth. Materials and Methods In this study, photographs of 168 teeth belonging to 20 patients aged between 10–15 years, who met our criteria were included. Intraoral photographs were taken of the patients in two stages, before and after the application of the plaque staining agent. To train the AI system to identify plaque on teeth with dental plaque that is not discolored, plaque and teeth were marked on photos with exposed dental plaque. 140 teeth were used to construct the training group, while 28 teeth were used to create the test group. Another dentist reviewed images of teeth with dental plaque that wasn't discolored, and the effectiveness of AI in detecting plaque was evaluated using pertinent performance indicators. To compare the AI model and the dentist's evaluation outcomes, the mean IoU values were evaluated by the Wilcoxon test. Results The AI system showed higher performance in our study with a precision of 82% accuracy, 84% sensitivity, 83% F1 score, 87% accuracy, and 89% specificity in plaque detection. The AUC value was found to be 0.922, and the IoU value was 76%. Subsequently, the dentist's plaque diagnosis performance was also evaluated. The IoU value was 0.71, and the AUC was 0.833. The artificial intelligence model showed statistically significantly higher performance than the dentist. (p < 0.05) Conclusions The AI algorithm that we developed has achieved promising results and demonstrated clinically acceptable performance in detecting dental plaque compared to a dentist.