# Topology of a Smile: Persistent Homology in Dental Imaging

arXiv: https://arxiv.org/abs/2608.21422 · HTML: https://mlsanity.com/paper/2608.21422/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 30 references that are in the map.
- First posted on arXiv 14 Aug 2026.

## Builds on (most cited first, 4 of 4)

- [Persistence Images: A Stable Vector Representation of Persistent Homology](https://mlsanity.com/paper/1507.06217/) (2015)
- [Diffusion-Based Hierarchical Multi-Label Object Detection to Analyze Panoramic Dental X-rays](https://mlsanity.com/paper/2303.06500/) (2023)
- [PHG-Net: Persistent Homology Guided Medical Image Classification](https://mlsanity.com/paper/2311.17243/) (2023)
- [Coarse-to-fine volumetric segmentation of teeth in Cone-Beam CT](https://mlsanity.com/paper/1810.10293/) (2018)

## Abstract (arXiv)

CBCT (Cone Beam Computed Tomography) scans provide detailed three-dimensional images, widely used in dentistry for diagnostic and treatment planning tasks. While invaluable, analyzing and documenting these scans is labor-intensive, prompting efforts to automate key steps like the classification and segmentation of anatomical structures to identify tooth types and associated pathologies. In this article, we propose an approach to automation that leverages persistent homology, a framework from topological data analysis that studies the shape of data by identifying features like connected components, holes, and voids across multiple scales. Persistent homology, together with a support vector machine, allows us to classify teeth in a CBCT scan and to perform diagnostics. Our method advances the state of the art, reaching average accuracy scores of 97.67% for tooth-labeling and 96.77% for diagnostic tasks, outperforming a CNN trained on the same data with accuracy of 70.27% and 86.67%, respectively.