# Dental pathology detection in 3D cone-beam CT

arXiv: https://arxiv.org/abs/1810.10309 · HTML: https://mlsanity.com/paper/1810.10309/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2018.

## Built on by (most cited first, 1 of 1)

- [An efficient method to automate tooth identification and 3D bounding box extraction from Cone Beam CT Images](https://mlsanity.com/paper/2407.05892/) (2024)

## Abstract (arXiv)

Cone-beam computed tomography (CBCT) is a valuable imaging method in dental diagnostics that provides information not available in traditional 2D imaging. However, interpretation of CBCT images is a time-consuming process that requires a physician to work with complicated software. In this work we propose an automated pipeline composed of several deep convolutional neural networks and algorithmic heuristics. Our task is two-fold: a) find locations of each present tooth inside a 3D image volume, and b) detect several common tooth conditions in each tooth. The proposed system achieves 96.3% accuracy in tooth localization and an average of 0.94 AUROC for 6 common tooth conditions.