Topology of a Smile: Persistent Homology in Dental Imaging
arXiv:2608.21422v1 Announce Type: new Abstract: 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.
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[Submitted on 14 Aug 2026]
Title:Topology of a Smile: Persistent Homology in Dental Imaging
View a PDF of the paper titled Topology of a Smile: Persistent Homology in Dental Imaging, by Leon Dahlmeier and 3 other authors
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Abstract: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.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Algebraic Topology (math.AT)
Cite as: arXiv:2608.21422 [cs.CV]
(or arXiv:2608.21422v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2608.21422
arXiv-issued DOI via DataCite
Submission history
From: Leon Dahlmeier [view email] [v1] Fri, 14 Aug 2026 18:22:30 UTC (667 KB)
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