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Automated detection and federation dentaire internationale-based identification of teeth in mixed dentition panoramic
Rahul Kumar Singh1, Sukhdeep Singh2, Neha Awasthi3
1Clinical Process Manager, Amwaaj Dental Center, Salmiya, Kuwait.
Background:
Accurate detection and numbering of teeth on panoramic radiographs are essential for diagnosis and treatment planning. Manual interpretation is time-consuming and prone to variability, particularly in mixed dentition. Deep learning offers potential for reliable automated analysis.
Aim And Objectives:
To develop and evaluate a deep learning-based model for automated detection and Fédération Dentaire Internationale (FDI)-based identification of teeth in mixed dentition panoramic radiographs. Objectives included tooth segmentation, detection, and automatic numbering.
Materials And Methods:
A retrospective dataset of 670 pediatric panoramic radiographs (6-12 years) was used. Images were annotated using semantic and instance segmentation and divided into training (80%), validation (10%), and test (10%) sets. A YOLOv11 instance segmentation model was trained using transfer learning. Performance was assessed using precision, recall, F1-score, and mean average precision (mAP).
Results:
The model demonstrated excellent performance with precision, recall, and F1-score of approximately 99.8%. mAP values reached ~99-100% at IoU 0.5 and ~98-99% at IoU 0.5-0.95. Slightly reduced recall was observed for third molars.
Conclusion:
The proposed model provides highly accurate automated tooth detection and FDI-based numbering in mixed dentition, with strong potential for integration into clinical dental workflows.
