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.
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AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
two deep learning models, YOLO and Mask-RCNN, will be trained on this dataset to accurately detect and classify images showing signs of secondary caries
Timeframe: baseline
Heba-Tullah mohamed mansour, master