A novel deep learning-based model for automated tooth detection and numbering in mixed and permanent dentition in occlusal photographs
Our study advances dental diagnostics by developing a highly precise artificial intelligence model for detecting and numbering primary and permanent teeth on occlusal photographs. The model's performance, highlights its potential for real-world applications, including tele-dentistry and epidemiological studies in underserved areas. The model could be integrated with other systems to identify dental problems such as caries and orthodontic issues.
Authors
- Zahra Ghorbani (ORCID: https://orcid.org/0000-0002-3586-8913)
- Seyed Sepehr Mirebeigi-Jamasbi (ORCID: https://orcid.org/0000-0001-7589-8220)
- Mohammad Hassannia Dargah (ORCID: https://orcid.org/0009-0006-0521-937X)
- Mohammad Nahvi (ORCID: https://orcid.org/0000-0002-6133-8529)
- Sara Alsadat Hosseinikhah Manshadi (ORCID: https://orcid.org/0009-0000-5737-2110)
- Zeinab Akbarzadeh Fathabadi (ORCID: https://orcid.org/0009-0004-2153-2388)
Institutions
- Shahid Beheshti University (IR)
- Shahid Beheshti University of Medical Sciences (IR)
Publication Details
- Journal
- BMC Oral Health
- Published
- 2025-03-29
- DOI
- https://doi.org/10.1186/s12903-025-05803-y
- Citations
- 10
- Primary Topic
- Dental Radiography and Imaging
- Type
- article
- Field-Weighted Citation Impact
- 17.91