Advancing periodontal diagnosis: harnessing advanced artificial intelligence for patterns of periodontal bone loss in cone-beam computed tomography
This study employed AI models on CBCT images to automatically detect tooth presence, numbering, and various periodontal bone defects, achieving high accuracy and demonstrating potential for enhancing dental diagnostics and patient care.
Authors
- Kaan Orhan (ORCID: https://orcid.org/0000-0001-6768-0176)
- Rohan Jagtap (ORCID: https://orcid.org/0000-0002-9115-7235)
- Özer Çelik (ORCID: https://orcid.org/0000-0002-4409-3101)
- İbrahim Şevki Bayrakdar (ORCID: https://orcid.org/0000-0001-5036-9867)
- Sevda Kurt‐Bayrakdar (ORCID: https://orcid.org/0000-0002-3711-6520)
- Alican Kuran (ORCID: https://orcid.org/0000-0001-9677-8690)
Institutions
- Ankara University (TR)
- University of Mississippi Medical Center (US)
- Eskişehir Osmangazi University (TR)
- Kocaeli Üniversitesi (TR)
Publication Details
- Journal
- Dentomaxillofacial Radiology
- Published
- 2025-02-05
- DOI
- https://doi.org/10.1093/dmfr/twaf011
- Citations
- 15
- Primary Topic
- Dental Radiography and Imaging
- Type
- article
- Field-Weighted Citation Impact
- 26.86
Funders
- Türkiye Bilimsel ve Teknolojik Araştırma Kurumu