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Updated: Jan 26, 2026

Accuracy in Dental Medicine, A New Way to Measure Trueness and Precision
Published on: April 29, 2014
AI-powered detection of dental anatomy: a YOLO-based approach
Hatice Tekis1, Taha Zirek2, Melek Tassoker2
1Faculty of Engineering, Department of Computer Engineering, Eskisehir Technical University, Eskişehir, Turkey; Cappadocia Vocational School, Information Security Technology Program, Cappadocia University, Nevşehir, Türkiye.
Objective:
This study aims to develop an AI-powered detection system for identifying dental anatomy-specifically tooth numbers and names-using YOLO (You Only Look Once) models to enhance diagnostic efficiency and automation in dentistry STUDY DESIGN: An annotated dataset of 724 high-resolution digital intraoral dental photographs (505 for training, 112 for validation, 107 for testing), obtained from Kaggle and Roboflow, was used. Multiple YOLO versions (YOLOv8l, YOLOv9c, YOLOv10l, YOLO11l) were implemented. Model performance was evaluated based on recall, precision, F1 score, and mean Average Precision (mAP@50).
Results:
YOLOv8l achieved the highest F1 score (96.7%) and recall (97.8%). YOLO11l yielded the best precision (96.6%) and highest mAP@50 (98.4%). All models demonstrated strong potential in detecting teeth accurately from high-resolution images.
Conclusion:
YOLO-based models offer an effective solution for automatic detection of dental anatomy. Their integration into telemedicine and digital dentistry platforms can streamline diagnostic workflows, reduce manual workload, and expand access to dental care, particularly in underserved regions.
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