Artificial Intellegence Rivals Digital Bitewing in Detect Secondary Caries

This study uses digital bitewing radiography as a standard for diagnosing proximal secondary caries. Patients will undergo imaging with a parallel technique and fixed settings to ensure high-quality, consistent images. Radiographs are interpreted by experienced dental professionals to maintain diagnostic accuracy. Machine learning models YOLO and Mask-RCNN will analyze these images in three phases: pre-analytical, analytical, and post-analytical. A dataset of 322 labeled images, annotated by experts, is used to train these models. Data augmentation methods enhance model performance, and accuracy is assessed against radiographic results to confirm reliability.