Dental Anatomy Detection with YOLOv11: Deep Learning-Based Object Detection in Intraoral Radiographs

Abstract

Early diagnosis is crucial for effectively and healthily treating teeth and gums. There is a need for expert dentists in the field to diagnose and treat diseases by considering anatomical structures. This study involves the application of the YOLOv11 deep learning algorithm for automatically detecting dental anatomical structures in intraoral dental radiographs. In the research, the “Dental_Dataset_Level3” dataset, which includes seven different anatomical tooth classes (First Molar, First Premolar, Second Molar, Second Premolar, Canine, Central Incisor, and Lateral Incisor) and consists of a total of 724 images, was used. As a result of the experimental findings, the model achieved performance metrics of $\mathbf{9 7. 7 6 \%} \boldsymbol{\mathrm { mAP }} \text{@} \mathbf{5 0, ~ 7 9. 6 2 \%} \mathbf{m A P} \text{@} 50-95,94.94 \%$ precision, 97.11% recall, 96.02% F1-Score, and 92.34% accuracy.

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