Comparative Performance of <scp>YOLO</scp> Models in the Automated Segmentation of Dental Traumas on Panoramic Radiographs
The YOLOv12-based deep learning model demonstrated better performance in detecting dental trauma in pediatric panoramic radiographs compared to other models. Nevertheless, artificial intelligence has not yet achieved flawless performance in Dental Traumatology. Therefore, AI tools should be developed in collaboration with expert dentists to better support clinical decision-making in dental trauma cases.
Authors
- Kaan Orhan (ORCID: https://orcid.org/0000-0001-6768-0176)
- Münevver Kılıç (ORCID: https://orcid.org/0000-0002-3470-0430)
- Zehra Güner (ORCID: https://orcid.org/0000-0002-5309-6371)
- Özer Çelik (ORCID: https://orcid.org/0000-0002-4409-3101)
- İbrahim Şevki Bayrakdar (ORCID: https://orcid.org/0000-0001-5036-9867)
- Alican Kuran (ORCID: https://orcid.org/0000-0001-9677-8690)
- Ömer Faruk Kaygısız (ORCID: https://orcid.org/0000-0002-9903-0597)
- Derya Sarıoğlu
- Saadet Nilsu Muttepe
Institutions
- Ankara University (TR)
- Biruni University (TR)
- Eskişehir Osmangazi University (TR)
- Kocaeli Üniversitesi (TR)
- Gaziantep University (TR)
Publication Details
- Journal
- Dental Traumatology
- Published
- 2025-11-26
- DOI
- https://doi.org/10.1111/edt.70034
- Primary Topic
- Dental Trauma and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.00