{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T04:59:56Z","timestamp":1768625996463,"version":"3.49.0"},"reference-count":17,"publisher":"BMJ","issue":"1","license":[{"start":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T00:00:00Z","timestamp":1768521600000},"content-version":"unspecified","delay-in-days":15,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Novartis Research"},{"DOI":"10.13039\/501100003803","name":"University of Hong Kong","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003803","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hong Kong Society of Rheumatology"}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["RMD Open"],"accepted":{"date-parts":[[2026,1,2]]},"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To develop a deep learning algorithm for semiquantification of spinal inflammation in patients with axial spondyloarthritis (SpA).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The study included 330 participants with axial SpA. All patients underwent whole spine MRI with short \u03c4 inversion recovery (STIR) sequence by 3T MR unit. Three independent readers identified regions of interest to locate bone marrow oedema (BMO) and performed Spondyloarthritis Research Consortium of Canada (SPARCC) scoring. Two deep learning models based on attention Unet were developed. The BMO model differentiated image with or without spinal inflammation. The vertebral body (VB)-intervertebral disc (IVD) model identified discovertebral units for localisation. The intraclass correlation coefficient (ICC) and Pearson coefficient were used to evaluate agreement and correlation between scorings by human readers and deep learning-based pipeline. Performance of the models was evaluated using sensitivity, specificity, accuracy and Dice coefficient.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The ICC and the Pearson coefficient of SPARCC scores between human readers and the deep learning-based scoring pipeline were 0.80 and 0.82, respectively. The sensitivity and specificity of spinal inflammation identification were 0.90 and 0.84, respectively. The Dice coefficients were 0.81 (VB) and 0.80 (IVD) in images with spinal inflammation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The high consistency of the scoring pipeline with human readers suggested that the deep learning-based algorithm has the potential to provide semiquantitative assessment of spinal inflammation based on SPARCC in axial SpA.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/rmdopen-2025-006403","type":"journal-article","created":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T19:15:48Z","timestamp":1768590948000},"page":"e006403","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":0,"title":["Deep learning algorithm for semiquantification of spinal inflammation in axial spondyloarthritis"],"prefix":"10.1136","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1352-7181","authenticated-orcid":false,"given":"Yingying","family":"Lin","sequence":"first","affiliation":[{"name":"Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, Hong Kong"},{"name":"Department of radiology, Queen Mary Hospital, Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0175-1346","authenticated-orcid":false,"given":"Ho Yin","family":"Chung","sequence":"additional","affiliation":[{"name":"Division of Rheumatology and Clinical Immunology, Department of Medicine, The University of Hong Kong, Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1116-1171","authenticated-orcid":false,"given":"Cao","family":"Peng","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kam Ho","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of radiology, Queen Mary Hospital, Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vince Wing Hang","family":"Lau","sequence":"additional","affiliation":[{"name":"Division of Rheumatology and Clinical Immunology, Department of Medicine, The University of Hong Kong, Hong Kong, Hong Kong"},{"name":"Glenagles Hospital, Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seda Nur","family":"Aydo\u011fdu","sequence":"additional","affiliation":[{"name":"Division of Rheumatology and Clinical Immunology, Department of Medicine, The University of Hong Kong, Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7285-190X","authenticated-orcid":false,"given":"Eugenia Yue Tian","family":"Lung","sequence":"additional","affiliation":[{"name":"LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Brian De Yu","family":"Ma","sequence":"additional","affiliation":[{"name":"LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiqiang","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Radiology, The University of Hong Kong\u2014Shenzhen Hospital, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shirley CW","family":"Chan","sequence":"additional","affiliation":[{"name":"Division of Rheumatology and Clinical Immunology, Department of Medicine, The University of Hong Kong, Hong Kong, Hong 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Attention u-net: learning where to look for the pancreas. 2018."},{"key":"2026011611154214000_12.1.e006403.17","doi-asserted-by":"crossref","first-page":"276","DOI":"10.11613\/BM.2012.031","article-title":"Interrater reliability: the kappa statistic","volume":"22","author":"McHugh","year":"2012","journal-title":"Biochem Med (Zagreb)"}],"container-title":["RMD Open"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.1136\/rmdopen-2025-006403","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T19:15:55Z","timestamp":1768590955000},"score":1,"resource":{"primary":{"URL":"https:\/\/rmdopen.bmj.com\/lookup\/doi\/10.1136\/rmdopen-2025-006403"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":17,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1,16]]},"published-print":{"date-parts":[[2026,1]]}},"alternative-id":["10.1136\/rmdopen-2025-006403"],"URL":"https:\/\/doi.org\/10.1136\/rmdopen-2025-006403","relation":{},"ISSN":["2056-5933"],"issn-type":[{"value":"2056-5933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1]]}}}