Deep learning-enhanced detection of dental restorations and orthodontic appliances in panoramic radiographs using a clinically annotated dataset

Abstract

Objective: Manual interpretation of dental panoramic radiographs is labor intensive and prone to diagnostic fatigue, particularly in high-volume settings. While artificial intelligence offers potential solutions, existing automated detection models often suffer from limited generalization due to small-scale, inconsistent datasets and scale mismatches between generic algorithms and dental structures. This study aimed to develop a deep learning framework trained on a clinically annotated retrospective dataset to improve the multiclass detection accuracy of dental restorations and appliances.

Material and method: A retrospective dataset comprising 2,434 anonymized panoramic X-ray images was curated and annotated by trained dental professionals for six categories: crowns, root canal filling, fillings, bridges, implants, and braces. The baseline dataset used for contextual comparison had a different annotation scope and class composition from the enhanced dataset; therefore, it was used only as a descriptive reference rather than as a directly matched comparator. A faster Region based Convolutional Neural Network (R-CNN) model with a ResNet-50 Feature Pyramid Network backbone was trained using domain-specific optimizations, including customized anchor resizing (16–256 pixels) and adaptive data augmentation.

Findings: The enhanced dataset setting achieved an mAP@0.5 of 0.75, which was numerically higher than the baseline setting but should not be interpreted as a directly controlled improvement because the datasets differed in source and annotation scope. The system demonstrated significant gains in detecting small targets, with average recall increasing from 0.59 to 0.81. Performance varied by category; implants achieved the highest precision at 0.93, while braces exhibited lower precision at 0.32 due to overlapping artifacts. The average processing time was under 1.2 s per image on the tested laptop workstation.

Conclusion: These findings suggest that clinically curated data and domain-adaptive design may improve detection performance in panoramic radiography. The framework may have potential as a decision-support tool, but further external and multicenter validation is needed before clinical deployment.