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Abstract

Dental X-ray imaging is a critical diagnostic tool for identifying various dental anomalies. However, manual interpretation is time-consuming, prone to human error, and requires specialized expertise. Deep learning models, particularly object detection frameworks like YOLO, have demonstrated promising results in automating medical image analysis. This study aims to develop and evaluate a YOLOv8-based deep learning model for automated detection and classification of 14 dental anomaly categories, including Caries, Crowns, Fillings, Implants, and Periapical lesions. The proposed approach addresses limitations in previous YOLO versions by leveraging anchor-free detection and enhanced feature extraction for improved accuracy. The model was trained on a dataset of annotated dental X-ray images and preprocessed with data augmentation techniques to improve generalization. Performance was evaluated using Precision, Recall, F1-score, and Mean Average Precision (mAP). Additional insights were obtained from confusion matrices, precision-recall curves, and training-validation loss curves. The model achieved high precision in detecting Implants (0.90), Crowns (0.89), and Root Canal Treatment (0.69), demonstrating strong potential for clinical applications. However, Caries (0.30) and Periapical lesions (0.15) were detected with lower accuracy, indicating the need for further optimization. Analysis of training loss curves and label distributions suggested that class imbalance and anomaly co-occurrence influenced detection performance. YOLOv8 presents a promising AI-based solution for dental anomaly detection, capable of improving diagnostic efficiency and accuracy in clinical practice. The model’s integration into dental healthcare systems can reduce radiologists' workload and enhance early disease detection, particularly in resource-limited settings.

DOI

10.17977/um018v7i22024p164-175

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