AI-Based Automated Segmentation of the Mandibular Cortex and Mental Foramen for Radiomorphometric Measurement on Panoramic Radiographs: A Feasibility Study

Background/Objectives: This study evaluated the feasibility of a YOLOv5-based model for automated segmentation of these structures and automated measurement of mandibular cortical width (MCW) and panoramic mandibular index PMI.

Methods

Five hundred panoramic radiographs were retrospectively collected. The mandibular cortex and the right and left mental foramina were annotated with polygon labels as three separate classes and verified by an experienced radiologist; intra- and inter-observer reliability was evaluated on a random 10% subset. A YOLOv5s-seg model was trained for 500 epochs and evaluated on an independent, patient-level test set. Model performance was quantified with precision, recall, F1 score, Dice coefficient, mean intersection over union (mIoU), and mean average precision (mAP@0.5 and mAP@0.5:0.95).