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To begin with, the patients were split into training (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009753) and validation (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009189) cohorts. Secondly, multiple assessors manually extract the features of active inflammation (bone marrow edema) and structural lesions (erosions, sclerosis, ankylosis, joint space changes, and fat lesions). Meanwhile, we utilize 11 machine learning models and TabNet to develop imaging models, which contain six clinical risk factors for clinical models and combined clinical\u2010imaging models. Finally, the diagnostic performance of the aforementioned models was evaluated in the validation cohort including accuracy, area under the receiver operating characteristic curve (AUC), sensitivity, specificity, F1\u2010score, and Matthew's correlation coefficient (MCC).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Six features were extracted from the imaging findings. The combined clinical\u2010imaging models outperform the clinical and imaging models. In contrast, the combined clinical\u2010imaging model via TabNet (CCMRT) achieved the optimal AUC of 0.93(95% CI: 0.89, 0.97). Furthermore, it is observed that the bilateral joint space changes and right\u2010sided erosions, HLA\u2010B27 positivity, and CRP values significantly affected axSpA diagnostic prediction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The prediction model based on clinical risk factors and SIJ\u2010MRI imaging features can distinguish axSpA and non\u2010axSpA effectively. In addition, the TabNet demonstrates superior diagnostic efficacy compared with machine learning models.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/1756-185x.70004","type":"journal-article","created":{"date-parts":[[2024,12,18]],"date-time":"2024-12-18T00:34:46Z","timestamp":1734482086000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["The\n                    <scp>TabNet<\/scp>\n                    Model for Diagnosing Axial Spondyloarthritis Using\n                    <scp>MRI<\/scp>\n                    Imaging Findings and Clinical Risk Factors"],"prefix":"10.1111","volume":"27","author":[{"given":"Zhaojuan","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Information Engineering China Jiliang University  Zhejiang Hangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-6905-6706","authenticated-orcid":false,"given":"Yiling","family":"Pan","sequence":"additional","affiliation":[{"name":"College of Information Engineering China Jiliang University  Zhejiang Hangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanjie","family":"Lu","sequence":"additional","affiliation":[{"name":"Institute of Intelligent Media Computing Hangzhou Dianzi University  Zhejiang Hangzhou China"},{"name":"Shangyu Institute of Science and Engineering Co. Ltd. Hangzhou Dianzi University  Shaoxing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lusi","family":"Ye","sequence":"additional","affiliation":[{"name":"Department of Rheumatology The First Affiliated Hospital of Wenzhou Medical University  Wenzhou Zhejiang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mo","family":"Zheng","sequence":"additional","affiliation":[{"name":"Department of Radiology The First Affiliated Hospital of Wenzhou Medical University  Wenzhou Zhejiang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guodao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Intelligent Media Computing Hangzhou Dianzi University  Zhejiang Hangzhou China"},{"name":"Shangyu Institute of Science and Engineering Co. Ltd. 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