Automated chart filing on bitewings using deep learning: enhancing clinical diagnosis in a multi-center study
The proposed model provided fully automated tooth segmentation and numbering, along with comprehensive segmentation of dental conditions. Dental professionals can benefit from this model for reducing manual workload and enhancing clinical diagnosis.
Authors
- Eduardo Trota Chaves (ORCID: https://orcid.org/0000-0002-5313-4980)
- Stefaan Bergé
- Lingyun Cao (ORCID: https://orcid.org/0000-0001-6409-6708)
- Bas Loomans (ORCID: https://orcid.org/0000-0002-6684-9723)
- Tong Xi (ORCID: https://orcid.org/0000-0002-9142-9253)
- Niels van Nistelrooij
- Maximiliano Sérgio Cenci (ORCID: https://orcid.org/0000-0002-2543-6201)
- Shankeeth Vinayahalingam (ORCID: https://orcid.org/0000-0002-2679-3841)
Institutions
- Radboud University Nijmegen (NL)
- Universidade Federal de Pelotas (BR)
- Radboud University Medical Center (NL)
- Humboldt-Universität zu Berlin (DE)
- Radboud Institute for Molecular Life Sciences (NL)
Publication Details
- Journal
- Journal of Dentistry
- Published
- 2025-06-21
- DOI
- https://doi.org/10.1016/j.jdent.2025.105919
- Citations
- 2
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 4.96
Funders
- Radboud Universiteit
- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
- China Scholarship Council
- Radboud Universitair Medisch Centrum
- Berlin Institute of Health