Intelligent Detection of Residual Carious Tissue During Caries Removal Using an Improved YOLOv8 Model

Objective :This study aimed to develop a prototype of an AI-based recognition system for identifying residual carious tissue during caries removal by improving the YOLOv8 model. The goal was to provide preliminary theoretical and technical support for real-time AI-assisted guidance and localization of residual caries. Methods :A total of 100 extracted teeth with carious lesions were collected, numbered, and randomly assigned to training, validation, and test sets at a ratio of 7:1:2. During the extracoronal caries removal process for each tooth, 4 to 7 consecutive images were captured until all carious tissue was removed. Carious regions in the training images were manually annotated and used to train the improved YOLOv8 model. The model's predictions on the test set were compared with those made by standardized, experienced dental professionals. Performance was evaluated using accuracy, sensitivity, and specificity. Results :A total of 666 images were collected, including 566 images with carious lesions and 100 without. Compared with expert annotations, the improved YOLOv8 model achieved an accuracy of 87.9%, sensitivity of 88.4%, and specificity of 85% in identifying the presence of residual caries. For the localization of specific residual carious sites, the model achieved an accuracy of 92.8%, sensitivity of 93.2%, and specificity of 91.9%. Conclusion :The improved YOLOv8 model demonstrated strong capability in detecting and localizing residual carious tissue across consecutive images with high stability, indicating promising potential for clinical application.