Deep learning-based 3D automatic segmentation of impacted canines in CBCT scans
The results suggest that the nnU-Net v2-based deep learning model can effectively and autonomously segment impacted canines in CBCT volumes. Its strong performance highlights the potential of artificial intelligence to improve diagnostic efficiency in dentomaxillofacial radiology.
Authors
- Fatma Nur Kızılay (ORCID: https://orcid.org/0000-0001-6588-663X)
- Semanur Özüdoğru (ORCID: https://orcid.org/0000-0001-7967-9121)
- Özer Çelik (ORCID: https://orcid.org/0000-0002-4409-3101)
- İbrahim Şevki Bayrakdar (ORCID: https://orcid.org/0000-0001-5036-9867)
- Prof. Kaan Orhan
- İbrahim Tevfik Gülşen (ORCID: https://orcid.org/0000-0002-1014-4417)
- Alican Kuran (ORCID: https://orcid.org/0000-0001-9677-8690)
- Emine Gülşen (ORCID: https://orcid.org/0000-0002-7968-2395)
- Türkan Ünal
- Mehmet Uğurlu
- Kadir Gördeli
Institutions
- Ankara University (TR)
- Inonu University (TR)
- Eskişehir City Hospital (TR)
- Alanya University (TR)
- Alanya Alaaddin Keykubat Üniversitesi (TR)
- Eskişehir Osmangazi University (TR)
- Kocaeli Üniversitesi (TR)
- Istanbul Medeniyet University (TR)
Publication Details
- Journal
- BMC Oral Health
- Published
- 2025-12-23
- DOI
- https://doi.org/10.1186/s12903-025-07117-5
- Primary Topic
- Dental Radiography and Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00