Tooth-level detection and mapping of dental pathologies on panoramic radiographs using YOLOv11 and RT-DETR.

This article describes a reproducible method for automated tooth-level detection and mapping of dental pathologies on panoramic radiographs. The workflow integrates two deep learning stages: (1) segmentation of individual teeth and (2) detection and classification of dental lesions. Outputs are combined to assign pathologies to specific teeth, supporting comprehensive radiological assessment. The method was implemented and tested using two datasets: a collection of 1,628 panoramic radiographs annotated with 12 pathology categories, and the public Tufts Dental Database. Several model architectures were evaluated, including YOLOv11 and RT-DETR configurations. The influence of image formatting and preprocessing strategies on performance was also assessed. Results showed that RT-DETR-x achieved the highest mean average precision (mAP@50) for pathology detection, while YOLOv11x produced the most accurate tooth segmentation. The integrated system demonstrated high precision in linking lesions to the corresponding teeth. This method offers a practical framework for developing AI-assisted diagnostic tools in dentistry and can be adapted to other imaging datasets. • Development of a fully automated dental diagnostic tool that segments individual teeth and maps detected pathologies to their corresponding tooth • YOLOv11 and RT-DETR variants are extensively tested for segmentation and detection tasks • Preprocessing and image formatting strategies are systematically compared.