Detection of Tooth caries in Bitewing Radiographs using Deep Learning
This work addresses the challenge of improving dental caries detection for dentists, though it is incremental as it applies existing deep learning methods to a specific medical imaging task.
The authors tackled the problem of detecting dental caries in bitewing radiographs by developing a deep learning-based Computer Aided Diagnosis system, which achieved higher recall and F1-score than certified dentists, with a dataset of over 3000 annotated radiographs.
We develop a Computer Aided Diagnosis (CAD) system, which enhances the performance of dentists in detecting wide range of dental caries. The CAD System achieves this by acting as a second opinion for the dentists with way higher sensitivity on the task of detecting cavities than the dentists themselves. We develop annotated dataset of more than 3000 bitewing radiographs and utilize it for developing a system for automated diagnosis of dental caries. Our system consists of a deep fully convolutional neural network (FCNN) consisting 100+ layers, which is trained to mark caries on bitewing radiographs. We have compared the performance of our proposed system with three certified dentists for marking dental caries. We exceed the average performance of the dentists in both recall (sensitivity) and F1-Score (agreement with truth) by a very large margin. Working example of our system is shown in Figure 1.