DMAF-Net: deformable multi-scale adaptive fusion network for dental structure detection with panoramic radiographs

Abstract

Objectives Panoramic radiography is one of the most commonly used diagnostic modalities in dentistry. Automatic recognition of panoramic radiography helps dentists in decision support. In order to improve the accuracy of the detection of dental structural problems in panoramic radiographs, we have improved the YOLO network and verified the feasibility of this new method in aiding the detection of dental problems. Methods We propose a Deformable Multi-scale Adaptive Fusion Net (DMAF-Net) to detect five types of dental situations (impacted teeth, missing teeth, implants, crown restorations and root canal-treated teeth) in panoramic radiography by improving the You Only Look Once (YOLO) network. In DMAF-Net, we propose different modules to enhance the feature extraction capability of the network as well as to acquire high-level features at different scales, while using adaptive spatial feature fusion to solve the problem of scale mismatches of different feature layers, which effectively improves the detection performance. In order to evaluate the detection performance of the models, we compare the experimental results of different models in the test set, and select the optimal results of the models by calculating the average of different metrics in each category as the evaluation criteria. Results 1474 panoramic radiographs were divided into training, validation and test sets in the ratio of 7:2:1. In the test set, the average precision and recall of DMAF-Net are 92.7% and 87.6%, respectively; the mean Average Precision (mAP0.5 and mAP [0.5:0.95]) are 91.8% and 63.7%, respectively. Conclusions The proposed DMAF-Net model improves existing deep learning models and achieves automatic detection of tooth structure problems in panoramic radiographs. This new method has great potential for new computer-aided diagnostic, teaching and clinical applications in the future.