Dual-Model Deep Learning Framework for Tooth Segmentation and Multi-Condition Detection in Panoramic Radiographs

Abstract

Orthopantomogram (OPG) radiographs are one of the main pillars of dental diagnosis, yet their interpretation is subjective, time consuming, and expensive depending on the radiologist. We suggest a deep learning structure to solve this by adding U-Net-based tooth segmentation and YOLOv8-based object detection to analyze the dental abnormalities in an automated manner. The segmentation model deals with anatomical localization and gives estimates of the regions of focus, and the detection model deals with clinically significant findings like cavities, impacted teeth, fillings and implants. The U-Net has 97.23% accuracy on validation data in the segmentation of teeth and the YOLOv8 model has a total mAP at 0.5 of 78.7% at 640 × 640, with the difference between individual classes rep-resenting imbalances in ourlimited dataset. One of the design considerations was clinical usability via continuous learning approach. The framework has an interactive course-correction interface where clinicians can manipulate segmentation masks and refine detection bounding boxes. An error-correction loop is built on top of these corrections, where both models are refined at each annotated case, as the number of annotated cases increases. The final output is an automatically generated PDF that displays patient details along with the dental analysis summary. The dental anomaly and the count of each type is displayed. Confidence scores for prediction can also be displayed depend-ing on the user’s choice. This end-to-end pipeline is practically feasible and cost effective while being useful in real-time for clinicians.