Enhanced diagnosis of axial spondyloarthritis using machine learning with sacroiliac joint MRI: a multicenter study
Axial spondyloarthritis (AxSpA) lacks definitive diagnostic criteria or markers, leading to diagnostic delay. MRI-based deep learning provided quantitative analysis of sacroiliac joint changes indicative of axSpA. A machine learning model combining sacroiliac joint MRI and clinical risk factors enhanced axSpA identification.
Authors
- Xinai Han (ORCID: https://orcid.org/0000-0002-3642-330X)
- Zefeiyun Chen
- Wei Yang (ORCID: https://orcid.org/0000-0003-2575-3570)
- Qian Yang (ORCID: https://orcid.org/0000-0003-3243-3153)
- Xiaohong Wang (ORCID: https://orcid.org/0000-0001-8557-0405)
- Bomiao Lin
- Xin Li (ORCID: https://orcid.org/0000-0002-5985-003X)
- Qiang Ye (ORCID: https://orcid.org/0000-0002-9018-8224)
- Yinghua Zhao (ORCID: https://orcid.org/0000-0001-9969-1670)
- Qiuxia Xie (ORCID: https://orcid.org/0000-0002-6501-6738)
- Zhuoyao Xie
- Yi He (ORCID: https://orcid.org/0009-0000-6484-100X)
- C Liu
Institutions
- Sun Yat-sen University (CN)
- Third Affiliated Hospital of Southern Medical University (CN)
- Sixth Affiliated Hospital of Sun Yat-sen University (CN)
- Zhujiang Hospital (CN)
- Third Affiliated Hospital of Sun Yat-sen University (CN)
- Southern Medical University (CN)
Publication Details
- Journal
- Insights into Imaging
- Published
- 2025-04-25
- DOI
- https://doi.org/10.1186/s13244-025-01967-x
- Citations
- 5
- Primary Topic
- Spondyloarthritis Studies and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 8.34
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Guangdong Province