ArticleScientific reports2026
Clinical investigation of a digital biomarker for joint swelling in inflammatory arthritis based on automated quantification of dorsal finger fold patterns.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
This study aimed to clinically evaluate a digital biomarker, the Finger Fold Index (FFI), derived from the ratio of joint diameter to finger fold surface area in hand photographs, for the assessment of joint swelling in inflammatory arthritis. 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 (0-3) 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 r = 0.443 (95% CI 0.384-0.498), while mean FFI values were weakly correlated with DAS28-CRP dichotomized at 3.2 (r = 0.310; 95% CI 0.123-0.475). ROC analysis showed moderate discriminative performance of the prediction models for PIP joint swelling (2-4), with AUCs ranging from 0.664 to 0.786 (95% CI 0.615-0.868). Notably, diagnostic performance was characterized by low specificity. FFI values exceeding the healthy cut-offs were associated with swelling (Cramer's V = 0.400-0.631; p < 0.001) 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.
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