Deep learning for tooth segmentation in cone-beam computed tomography: a systematic review

Maria Fernanda Escalante Freitez and Mafefreitez@gmail.com

SAP Artificial Intelligence in Dentistry, 2025

Abstract: Introduction: Tooth segmentation in cone-beam computed tomography is a prerequisite for digital treatment planning, but manual segmentation is slow and subjective. Deep learning has been proposed to automate it. Methods: Following PRISMA 2020, PubMed, OpenAlex, and Crossref were searched for studies published from 2018 to 2025 that evaluated deep learning models for tooth segmentation in cone-beam computed tomography against a reference standard. Risk of bias was assessed with QUADAS-2. Results: Twenty-two studies were included, most using U-Net or nnU-Net architectures. The Dice similarity coefficient ranged from 0.84 for impacted canines to 0.99, with most studies reporting values above 0.90. External validation was reported in a minority of studies. Conclusions: Deep learning segments teeth in cone-beam computed tomography with high Dice coefficients in most studies, but performance varies with tooth type and dataset, and external validation remains uncommon.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:cwf:aidart:aid202526

DOI: 10.62486/aid202526

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