Deep learning for automated alveolar cleft segmentation and bone graft volume estimation in cone-beam computed tomography imaging: a multicenter study
The deep learning-based tool that was developed in the present study can accurately perform automated segmentations of unilateral alveolar clefts and estimate the required bone graft volume.
Authors
- Susanne Brogårdh‐Roth (ORCID: https://orcid.org/0000-0003-2745-1816)
- Kristina Hellén‐Halme (ORCID: https://orcid.org/0000-0001-9885-0019)
- Magnus Becker (ORCID: https://orcid.org/0000-0002-1915-1736)
- Xie‐Qi Shi (ORCID: https://orcid.org/0000-0001-6967-4407)
- António Vicente (ORCID: https://orcid.org/0009-0007-0252-4165)
- Peng Ding (ORCID: https://orcid.org/0000-0001-7786-8703)
- Zhikang Xu (ORCID: https://orcid.org/0000-0002-8607-8209)
- Anna‐Paulina Wiedel (ORCID: https://orcid.org/0000-0001-6088-1143)
- Kuo Feng Hung (ORCID: https://orcid.org/0000-0002-3971-3484)
- Jiegang Yang (ORCID: https://orcid.org/0000-0002-3339-9885)
- Jian Li (ORCID: https://orcid.org/0000-0003-4171-9524)
Institutions
- Malmö University (SE)
- Prince Philip Dental Hospital (CN)
- Wuhan University (CN)
- MemorialCare Health System (US)
- Skåne University Hospital (SE)
- University of Bergen (NO)
- University of Hong Kong (HK)
Publication Details
- Journal
- Oral Surgery Oral Medicine Oral Pathology and Oral Radiology
- Published
- 2025-11-14
- DOI
- https://doi.org/10.1016/j.oooo.2025.10.020
- Primary Topic
- Cleft Lip and Palate Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00