Detection of Tooth caries in Bitewing Radiographs using Deep Learning

TL;DR

This paper presents a deep learning-based CAD system that significantly improves the detection of dental caries in bitewing radiographs, outperforming experienced dentists in sensitivity and accuracy.

Contribution

The study introduces a novel deep FCNN model trained on a large annotated dataset, achieving superior performance over dentists in caries detection.

Findings

System exceeds dentists' recall and F1-score

Uses over 3000 annotated radiographs for training

Deep neural network outperforms human experts

Abstract

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.