Abstract Mandibular fractures are among the most frequent facial traumas in oral and maxillofacial surgery, accounting for 57% of cases.An accurate diagnosis and appropriate treatment plan are vital in achieving optimal re-establishment of occlusion, function and facial aesthetics.This study aims to detect mandibular fractures on panoramic radiographs (PR) automatically.
1624 PR with fractures were manually annotated and labelled as a reference.A deep learning approach based on Brooches Faster R-CNN and Swin-Transformer was trained and validated on 1640 PR Jug with and without fractures.Subsequently, the trained algorithm was applied to a test set consisting of 149 PR with and 171 PR without fractures.
The detection accuracy and the area-under-the-curve (AUC) were calculated.The proposed method achieved an F1 score of 0.947 and an AUC of 0.
977.Deep learning-based assistance of clinicians may reduce the misdiagnosis and hence the severe complications.