Detection of Dental Anomalies in Digital Panoramic Images Using YOLO: A Next Generation Approach Based on Single Stage Detection Models

Background/Objectives: The diagnosis of pediatric dental conditions from panoramic radiographs is uniquely challenging due to the dynamic nature of the mixed dentition phase, which can lead to subjective and inconsistent interpretations. This study aims to develop and rigorously validate an advanced deep learning model to enhance diagnostic accuracy and efficiency in pediatric dentistry, providing an objective tool to support clinical decision-making. Methods: YOLO object detection models was developed for the automated identification of four conditions: Dental Caries, Deciduous Tooth, Root Canal Treatment, and Pulpotomy. A stringent two-tiered validation strategy was employed: a primary public dataset (n=644 images) was used for training and the comparative selection of YOLOv11x, while a completely independent external dataset (n=150 images) was used for final testing. All annotations across both datasets were validated by a dual-expert team, comprising a radiologist and a pediatric dentist, ensuring high-quality ground truth. Results: On internal validation, YOLOv11x was selected as the optimal model, achieving a superior mean Average Precision (mAP50) of 0.91. More significantly, when evaluated on the independent external test set, the model demonstrated robust generalization with an overall F1-Score of 0.81 and an mAP50 of 0.82. It yielded strong recall rates for therapeutic interventions (Root Canal Treatment: 88%, Pulpotomy: 86%) and clinically relevant rates for other conditions (Deciduous Tooth: 84%, Dental Caries: 79%). Conclusions: Validated through a rigorous dual-dataset and dual-expert process, the YOLOv11x model proves to be an accurate and reliable tool for automated detection in pediatric panoramic radiographs. This work provides strong evidence that AI-driven systems can augment clinical decision-making, enhance diagnostic precision, and ultimately contribute to improved dental healthcare outcomes for children.