Clinical Research Directory

Artificial Intellegence Rivals Digital Bitewing in Detect Secondary Caries

Sponsor: Cairo University

Summary

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.

Official title: AI Rivals Traditional Bite Wing Radiography in Detecting Proximal Secondary Caries in A Group of Egyptian Patients at Cairo University, Faculty OF Dentistry Hospital (Diagnostic Accuracy Study)

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Interventions