Deep Learning-Based Tooth Localization and Abnormality Detection on Panoramic Radiographs

Abstract

Automated analysis of panoramic radiographs remains challenging due to anatomical complexity and image variability. While deep learning has shown strong performance in dental imaging, most studies focus on isolated tasks. This study aimed to propose a hierarchical YOLOv8-based framework aligned for comprehensive analysis of panoramic radiographs using structured dental annotations. A three-stage deep learning pipeline based on YOLOv8 was developed using the DENTEX dataset. The framework includes (1) quadrant classification, (2) tooth enumeration (FDI 11-48), and (3) tooth-level abnormality detection using a two-stage approach (binary screening followed by subtype classification). Panoramic radiographs with hierarchical annotations were used, with an 80:20 train–validation split. Performance was evaluated using mAP50, mAP50-95, precision, recall, and F1-score. The model achieved near-perfect performance for quadrant classification (mAP50=0.994, mAP50-95=0.750, precision=0.994, recall=0.995, and F1=0.994) and strong performance for tooth enumeration (mAP50=0.936, mAP50-95=0.536, precision=0.902, recall=0.897, and F1=0.899). Abnormality detection showed moderate performance (mAP50=0.687, mAP50-95=0.480, precision=0.655, recall=0.742, and F1=0.696). At the class level, impacted teeth (F1=0.904) and caries (F1=0.885) were well detected, whereas periapical lesions (F1=0.568) and deep caries (F1=0.585) showed lower performance. Precision and recall were balanced across tasks. The proposed hierarchical framework enables anatomical localization and integration of detection tasks of panoramic radiographs within a unified pipeline using YOLOv8. While performance is near-ceiling for anatomical tasks, disease detection remains challenging, particularly for low-contrast lesions.