ArticleRheumatology advances in practice2025
Single-camera motion capture of finger joint mobility as a digital biomarker for disease activity in rheumatoid arthritis.
Article in Rheumatology advances in practice, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- AI detects signs of inflammatory arthritis in hand images.Nature reviews. Rheumatology · 2026Article
- Clinical investigation of a digital biomarker for joint swelling in inflammatory arthritis based on automated quantification of dorsal finger fold patterns.Scientific reports · 2026Article
- Artificial intelligence in rheumatology: days of a future past.Rheumatology advances in practice · 2025Article
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Authors and funding
7 authors.
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Abstract
Objective: To investigate the association between hand motion tracking features obtained through computer vision from smartphone cameras and disease activity in patients with RA. Methods: The PyPI package of MediaPipe (version 0.9.0.1) was used for key landmark detection. Finger joint angles were calculated in each frame using the normalized dot product of the vectors (equations). RA patients were instructed to perform a rapid repetition of five fist closures. Hand movements were captured using standard smartphone cameras. Kinetic features time to maximum flexion for MCP, PIP and DIP joints were correlated with RA disease activity and disability outcomes. Logistic regression was used to investigate associations of range of motion and kinetic features with 28-joint DAS (DAS28) low disease activity/remission. Results: Our model showed promising performance in predicting low disease activity/remission in RA patients. Internal validation using 5-fold cross-validation on the training dataset ( Conclusion: Single-camera motion capture of repeated fist closure may serve as an accessible digital biomarker for disease activity in RA.
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