# Detection of Tooth caries in Bitewing Radiographs using Deep Learning

arXiv: https://arxiv.org/abs/1711.07312 · HTML: https://mlsanity.com/paper/1711.07312/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2017.

## Built on by (most cited first, 3 of 3)

- [An End-to-end Entangled Segmentation and Classification Convolutional Neural Network for Periodontitis Stage Grading from Periapical Radiographic Images](https://mlsanity.com/paper/2109.13120/) (2021)
- [Interpretable and Interactive Deep Multiple Instance Learning for Dental Caries Classification in Bitewing X-rays](https://mlsanity.com/paper/2112.09694/) (2021)
- [AI-Dentify: Deep learning for proximal caries detection on bitewing x-ray -- HUNT4 Oral Health Study](https://mlsanity.com/paper/2310.00354/) (2023)

## Abstract (arXiv)

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.