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Smartphone hand photographs from patients with rheumatoid (RA) and psoriatic arthritis (PsA) were analyzed using a machine learning pipeline for automated detection and processing of joint diameter and finger folds at the proximal interphalangeal (PIP) joints. The FFI was clinically evaluated by correlation with joint swelling scores (0\u20133) and DAS28-CRP. A healthy cohort was used to establish FFI cut-offs, which were then compared to the arthritis cohorts. A total of 1275 PIP joint images of 124 arthritis patients and 53 healthy individuals were included. FFI values correlated with swelling scores in the arthritis population with\n                    <jats:italic>r<\/jats:italic>\n                    \u2009=\u20090.443 (95% CI 0.384\u20130.498), while mean FFI values were weakly correlated with DAS28-CRP dichotomized at 3.2 (\n                    <jats:italic>r<\/jats:italic>\n                    \u2009=\u20090.310; 95% CI 0.123\u20130.475). ROC analysis showed moderate discriminative performance of the prediction models for PIP joint swelling (2\u20134), with AUCs ranging from 0.664 to 0.786 (95% CI 0.615\u20130.868). Notably, diagnostic performance was characterized by low specificity. FFI values exceeding the healthy cut-offs were associated with swelling (Cramer\u2019s V\u2009=\u20090.400\u20130.631;\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.001) with the strongest association observed in cases of more pronounced swelling (grade 3). Longitudinal studies are needed to assess sensitivity to change and to establish whether this biomarker can be used for remote patient monitoring. Further refinement of the algorithm is warranted, as current diagnostic accuracy remains insufficient for clinical implementation. However, integration with additional clinical information, such as patient-reported outcomes, may improve performance and support future applicability in clinical practice.\n                  <\/jats:p>","DOI":"10.1038\/s41598-026-56797-8","type":"journal-article","created":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T22:54:48Z","timestamp":1780700088000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Clinical investigation of a digital biomarker for joint swelling in inflammatory arthritis based on automated quantification of dorsal finger fold patterns"],"prefix":"10.1038","volume":"16","author":[{"given":"Cinja","family":"Koller","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jules","family":"Maglione","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marc","family":"Blanchard","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arnd","family":"Kleyer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lukas","family":"Folle","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeroen","family":"Geurts","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"H\u00fcgle","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,6]]},"reference":[{"issue":"11","key":"56797_CR1","doi-asserted-by":"publisher","first-page":"680","DOI":"10.1038\/s41584-023-01008-1","volume":"19","author":"A Chan","year":"2023","unstructured":"Chan, A. & Knitza,. Remote monitoring in rheumatology: Seeing the right patient at the right time. Nat. Rev. Rheumatol. 19(11), 680\u2013681. https:\/\/doi.org\/10.1038\/s41584-023-01008-1 (2023).","journal-title":"Nat. Rev. Rheumatol."},{"issue":"4","key":"56797_CR2","doi-asserted-by":"publisher","DOI":"10.1136\/rmdopen-2023-003363","volume":"9","author":"P Hamann","year":"2023","unstructured":"Hamann, P., Knitza, J., Kuhn, S. & Knevel,. Recommendation to implementation of remote patient monitoring in rheumatology: Lessons learned and barriers to take. RMD Open. 9(4), e003363. https:\/\/doi.org\/10.1136\/rmdopen-2023-003363 (2023).","journal-title":"RMD Open."},{"issue":"12","key":"56797_CR3","doi-asserted-by":"publisher","DOI":"10.2196\/28285","volume":"9","author":"M Peyroteo","year":"2021","unstructured":"Peyroteo, M., Ferreira, I. A., Elvas, L. B., Ferreira, J. C. & Lap\u00e3o,. Remote monitoring systems for patients with chronic diseases in primary health care: Systematic review. 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The licence has been corrected to CC BY.","order":8,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"MB & TH are shareholders of Atreon SA. TH has filed a patent jointly with CHUV related to the image analysis for the monitoring of swelling of body parts and is scientific advisor of Vtuls.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"24727"}}