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arXiv Computer Vision By Leon Dahlmeier, Sara Kali\v{s}nik, Albert Mehl, Bastian Rieck

Topology of a Smile: Persistent Homology in Dental Imaging

Read the original on arXiv Computer Vision →

The article presents a method that uses persistent homology and a support vector machine to automatically classify teeth and diagnose pathologies in CBCT scans. It reports high accuracy, achieving 97.67% for tooth labeling and 96.77% for diagnostics, surpassing a CNN baseline. The approach aims to reduce the labor-intensive analysis of detailed 3‑D dental images.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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