Deep learning based quantitative cervical vertebral maturation analysis
This study presented a two-stage neural network that successfully automated the identification of cervical vertebral landmarks and the staging of QCVM. By streamlining the workflow and enhancing the accuracy of skeletal maturation estimation, this method offered valuable clinical support, particularly for practitioners with limited experience or access to advanced diagnostic resources, facilitating more consistent and reliable treatment planning.
Authors
- Fulin Jiang (ORCID: https://orcid.org/0000-0003-1332-6504)
- Abbas Ahmed Abdulqader (ORCID: https://orcid.org/0009-0006-4830-9746)
- Fangyuan Cheng (ORCID: https://orcid.org/0009-0005-6218-0232)
- Yan Yan (ORCID: https://orcid.org/0000-0002-3674-7160)
- Juan Li (ORCID: https://orcid.org/0000-0001-8320-2406)
- Tao Xiang (ORCID: https://orcid.org/0000-0002-2397-0531)
- Xin Chen (ORCID: https://orcid.org/0009-0005-7357-1511)
- Yong Qiu
- J. Yu (ORCID: https://orcid.org/0009-0007-5707-7956)
Institutions
- Chongqing University (CN)
- Sichuan University (CN)
- Stomatology Hospital (CN)
- Chongqing Three Gorges University (CN)
Publication Details
- Journal
- Head & Face Medicine
- Published
- 2025-03-26
- DOI
- https://doi.org/10.1186/s13005-025-00498-6
- Primary Topic
- Dental Radiography and Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00