Automated detection of dental restorations using deep learning on panoramic radiographs

Abstract

Objectives: Automatically detecting dental conditions using Artificial intelligence (AI) and reporting it visually are now a need for treatment planning and dental health management. This work presents a comprehensive computer-aided detection system to detect dental restorations. Methods: The state-of-art ten different deep-learning detection models were used including R-CNN, Faster R-CNN, SSD, YOLOv3, and RetinaNet as detectors. ResNet-50, ResNet-101, XCeption-101, VGG16, and DarkNet53 were integrated as backbone and feature extractor in addition to efficient approaches such Side-Aware Boundary Localization, cascaded structures and simple model frameworks like Libra and Dynamic. Total 684 objects in panoramic radiographs were used to detect available three classes, namely, dental restorations, denture and implant. Each model was evaluated by mean average precision (mAP), average recall (AR), and precision-recall curve using Common Objects in Context (COCO) detection evaluation metrics. Results: mAP varied between 0.755 and 0.973 for ten models explored while AR ranges between 0.605 and 0.771. Faster R-CNN RegnetX provided the best detection performance with mAP of 0.973 and AR of 0.771. Area under precision-recall curve was 0.952. Precision-recall curve indicated that errors were mainly dominated by localization confusions. Conclusions: Results showed that the proposed AI-based computer-aided system had great potential with reliable, accurate performance detecting dental restorations, denture and implant in panoramic radiographs. As training models with more data and standardization in reporting, AI-based solutions will be implemented to dental clinics for daily use soon.