Automated orthodontic diagnosis via self-supervised learning and multi-attribute classification using lateral cephalograms
This work contributes significantly to advancing automated diagnostic tools in orthodontics, addressing the critical need for accurate and efficient malocclusion diagnosis. The outcomes not only improve the efficiency and accuracy of diagnosis, but also have the potential to reduce healthcare costs associated with orthodontic treatments.
Authors
- Yuxing Bai (ORCID: https://orcid.org/0000-0002-7239-0891)
- Fan Wang (ORCID: https://orcid.org/0000-0001-5932-7567)
- Shaofeng Wang (ORCID: https://orcid.org/0000-0002-4959-2460)
- Shuang Liang (ORCID: https://orcid.org/0000-0001-7173-9847)
- Qiao Chang
- Xianju Xie
Institutions
- Capital Medical University (CN)
- Beijing YouAn Hospital (CN)
Publication Details
- Journal
- BioMedical Engineering OnLine
- Published
- 2025-02-04
- DOI
- https://doi.org/10.1186/s12938-025-01345-0
- Citations
- 4
- Primary Topic
- Dental Radiography and Imaging
- Type
- article
- Field-Weighted Citation Impact
- 7.16
Funders
- National Natural Science Foundation of China
- Capital Medical University
- Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support
- Beijing Municipal Natural Science Foundation