Akademik Veri Yönetim Sistemi
BMC ORAL HEALTH, cilt.1, sa.1, ss.1-22, 2026 (SCI-Expanded, Scopus)
This study aimed to evaluate and compare the performance of state-of-the-art deep learning-based object detection and segmentation architectures —YOLOv8, YOLOv11, Mask R-CNN and DeepLabV3—for automated tooth detection and numbering in children aged 6 to 12 years on panoramic radiographs.
A total of 1,378 anonymized panoramic images were retrospectively obtained and annotated using the FDI numbering system with polygon labeling. The dataset was stratified into age groups (6–12 years) to assess age-specific performance. All tested models (YOLOv8, YOLOv11, Mask R-CNN and DeepLabV3) were trained and evaluated in two scenarios: (1) overall detection performance without age separation and (2) age-based analysis. Evaluation metrics included Precision, Recall, F1 Score, mAP50, and mAP50-95.
In Scenario 1, YOLOv11 achieved higher scores across all metrics compared to YOLOv8, including Precision (0.8435), Recall (0.8755), F1 Score (0.8592), mAP50 (0.8715), and mAP50-95 (0.5613). Scenario 2 revealed performance variations across age groups, with YOLOv11 consistently outperforming YOLOv8. The highest performance was recorded at age 12 with YOLOv11, achieving 0.9657 F1 Score and 0.9817 mAP50, indicating enhanced accuracy in older children with more stable dentition.
YOLOv11 demonstrated superior capability in detecting and numbering teeth on pediatric panoramic radiographs, particularly in older age groups. These findings support the potential of advanced YOLO-based models as promising decision-support tools for tasks such as standardized charting and tooth identification during the mixed dentition period.