{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T23:28:09Z","timestamp":1781998089297,"version":"3.54.5"},"reference-count":39,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T00:00:00Z","timestamp":1779235200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T00:00:00Z","timestamp":1779235200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["acrjournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Arthritis &amp; Rheumatology"],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Regular imaging by conventional radiography to assess for joint damage is a cornerstone in the management of rheumatoid arthritis. Scoring systems to quantify such damage, such as the widely used Sharp\/van der Heijde (SvdH) score, are limited by the requirement of time and experienced staff as well as intra\u2010 and interrater variability. To alleviate these problems, autoscoRA, a fully automated scoring system to assign SvdH scores to radiographs of the hands and feet was developed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Using the hitherto largest data set of adult patients with rheumatoid arthritis, autoscoRA, a deep learning\u2010based system, was trained to automatically perform joint extraction and scoring of joint space narrowing and bone erosion.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The data set included 769 patients (155 of whom were in the test set) with 3,437 visits (707) and 12,144 radiographs (2,507). The model reached excellent agreement with a human scorer for joint space narrowing, erosion, and combined scores both on the joint level and for summed total SvdH scores (intraclass correlation 0.9). On a subset of data scored by a second human reader, the model outperformed the former in terms of agreement with the first human reader. In addition, autoscoRA demonstrated good agreement with a human reader for detecting longitudinal progression of joint damage across different SvdH score cutoffs defining the presence of progression (average agreement of 70%).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Automated systems like autoscoRA could be used to facilitate scoring of radiographic joint damage in clinical trials, registries, and observational studies, and, eventually, routine clinical care.<\/jats:p>\n                    <jats:p>\n                      <jats:boxed-text content-type=\"graphic\" position=\"anchor\">\n                        <jats:graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" mimetype=\"image\/png\" position=\"anchor\" specific-use=\"enlarged-web-image\" xlink:href=\"graphic\/art70196-gra-0001-m.png\">\n                          <jats:alt-text>image<\/jats:alt-text>\n                        <\/jats:graphic>\n                      <\/jats:boxed-text>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/art.70196","type":"journal-article","created":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T10:15:09Z","timestamp":1776766509000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["auto\n                    <scp>scoRA<\/scp>\n                    : Deep Learning to Automate Sharp\/van der Heijde Scoring of Radiographic Damage in Rheumatoid Arthritis"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1580-1316","authenticated-orcid":false,"given":"Thomas","family":"Deimel","sequence":"first","affiliation":[{"name":"Division of Rheumatology, Department of Medicine Medical University of Vienna  Vienna 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