{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,7]],"date-time":"2026-11-07T09:27:48Z","timestamp":1794043668576,"version":"4.8.0"},"reference-count":51,"publisher":"The Journal of Rheumatology","issue":"8","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Rheumatol"],"accepted":{"date-parts":[[2024,5,30]]},"published-print":{"date-parts":[[2024,8]]},"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>Psoriatic disease remains underdiagnosed and undertreated. We developed and validated a suite of novel, sensor-based smartphone assessments (Psorcast app) that can be self-administered to measure cutaneous and musculoskeletal signs and symptoms of psoriatic disease.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Participants with psoriasis (PsO) or psoriatic arthritis (PsA) and healthy controls were recruited between June 5, 2019, and November 10, 2021, at 2 academic medical centers. Concordance and accuracy of digital measures and image-based machine learning models were compared to their analogous clinical measures from trained rheumatologists and dermatologists.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Of 104 study participants, 51 (49%) were female and 53 (51%) were male, with a mean age of 42.3 years (SD 12.6). Seventy-nine (76%) participants had PsA, 16 (15.4%) had PsO, and 9 (8.7%) were healthy controls. Digital patient assessment of percent body surface area (BSA) affected with PsO demonstrated very strong concordance (Lin concordance correlation coefficient [CCC] 0.94 [95% CI 0.91-0.96]) with physician-assessed BSA. The in-clinic and remote target plaque physician global assessments showed fair-to-moderate concordance (CCC<jats:sub>erythema<\/jats:sub>0.72 [0.59-0.85]; CCC<jats:sub>induration<\/jats:sub>0.72 [0.62-0.82]; CCC<jats:sub>scaling<\/jats:sub>0.60 [0.48-0.72]). Machine learning models of hand photos taken by patients accurately identified clinically diagnosed nail PsO with an accuracy of 0.76. The Digital Jar Open assessment categorized physician-assessed upper extremity involvement, considering joint tenderness or enthesitis (AUROC 0.68 [0.47-0.85]).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The Psorcast digital assessments achieved significant clinical validity, although they require further validation in larger cohorts before use in evidence-based medicine or clinical trial settings. The smartphone software and analysis pipelines from the Psorcast suite are open source and freely available.<\/jats:p><\/jats:sec>","DOI":"10.3899\/jrheum.2024-0074","type":"journal-article","created":{"date-parts":[[2024,6,15]],"date-time":"2024-06-15T12:20:21Z","timestamp":1718454021000},"page":"781-789","source":"Crossref","is-referenced-by-count":18,"title":["Clinical Validation of Digitally Acquired Clinical Data and Machine Learning Models for Remote Measurement of Psoriasis and Psoriatic Arthritis: A Proof-of-Concept Study"],"prefix":"10.3899","volume":"51","author":[{"given":"Dan E.","family":"Webster","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7119-8136","authenticated-orcid":false,"given":"Rebecca 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