Using swin UNETR deep model for automated detection of alveolar bone fenestration/dehiscence in CBCT
The deep learning model enables precise localization and quantitative measurement of fenestration and dehiscence in CBCT images. This approach enhances diagnostic efficiency and accuracy in detecting fenestration and dehiscence, facilitating preoperative orthodontic risk assessment and personalized treatment planning.
Authors
- Hanxiao Huang (ORCID: https://orcid.org/0000-0002-4997-9913)
- Xu Che (ORCID: https://orcid.org/0000-0003-4193-3264)
- Ailin Xu
- Shan Dong
- Bin Zhang
Institutions
- Stomatology Hospital (CN)
- MEI Technologies (United States) (US)
Publication Details
- Journal
- Frontiers in Bioengineering and Biotechnology
- Published
- 2026-02-12
- DOI
- https://doi.org/10.3389/fbioe.2026.1752350
- Primary Topic
- Dental Radiography and Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Capital Medical University