Automated deep learning detection of orthodontically induced external apical root resorption in maxillary incisors on...

This study aimed to develop and compare two YOLOv12-based deep learning models-object detection and pose estimation-for the automatic classification of orthodontically induced external apical root resorption (OIEARR) using panoramic radiographs.

A total of 624 panoramic radiographs obtained from 312 patients aged 10-18 who underwent at least 12 months of fixed orthodontic treatment were retrospectively analyzed. Each maxillary central and lateral incisor was graded for OIEARR severity on a 4-point scale (Grade 0 to Grade 3) by two experienced orthodontists serving as the ground truth. Two YOLOv12-based models were trained: an object detection (OD) model for regional analysis and a pose estimation (PE) model for anatomical landmark localization. Both models were trained and validated on annotated panoramic images and evaluated using accuracy, precision, recall, specificity, F1-score, confusion matrix, and ROC-AUC.

The PE model outperformed the OD model across all evaluation metrics, demonstrating superior performance in detecting OIEARR. Specifically, the PE model achieved a weighted F1-score of 0.88, compared to 0.60 for the OD model. It also showed higher accuracy (0.93 vs. 0.78), precision (0.88 vs. 0.64), and recall (0.88 vs. 0.59), confirming its robustness in root resorption classification. Particularly in Grade 1 and Grade 2 resorption categories, the PE model demonstrated markedly superior classification performance (F1 = 0.85 and 0.88, respectively), while maintaining excellent detection in Grade 3 cases (F1 = 0.95). Confusion matrix analysis revealed that most misclassifications occurred between neighboring grades. ROC-AUC values for the PE model were consistently high (0.90-0.99), indicating strong discriminative ability across all resorption stages.

The YOLOv12x PE model offers a reliable and sensitive tool for detecting varying degrees of root resorption on panoramic radiographs. Its fine-grained anatomical localization capabilities provide an advantage for early diagnosis, making it a promising approach for clinical decision support in orthodontics.