The <scp>TabNet</scp> Model for Diagnosing Axial Spondyloarthritis Using <scp>MRI</scp> Imaging Findings and Clinical Risk Factors
The prediction model based on clinical risk factors and SIJ-MRI imaging features can distinguish axSpA and non-axSpA effectively. In addition, the TabNet demonstrates superior diagnostic efficacy compared with machine learning models.
Authors
- Guodao Zhang (ORCID: https://orcid.org/0000-0002-6264-5854)
- Mo Zheng
- Yanjie Lu (ORCID: https://orcid.org/0000-0002-0283-3167)
- Yiling Pan (ORCID: https://orcid.org/0000-0001-7076-3335)
- Lusi Ye (ORCID: https://orcid.org/0000-0003-3247-641X)
- Dan Chen (ORCID: https://orcid.org/0000-0003-2396-0314)
- Zhaojuan Zhang (ORCID: https://orcid.org/0000-0003-1662-2966)
Institutions
- Wenzhou Medical University (CN)
- First Affiliated Hospital of Wenzhou Medical University (CN)
- Hangzhou Dianzi University (CN)
- China Jiliang University (CN)
Publication Details
- Journal
- International Journal of Rheumatic Diseases
- Published
- 2024-12-01
- DOI
- https://doi.org/10.1111/1756-185x.70004
- Citations
- 3
- Primary Topic
- Spondyloarthritis Studies and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 1.41
Funders
- Natural Science Foundation of Zhejiang Province