ArticleBMC rheumatology2026
A deep learning system for diagnosis of rheumatoid arthritis on digital hand photographs.
Article in BMC rheumatology, 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
objectivesTo develop and evaluate a deep learning model for diagnosing untreated rheumatoid arthritis (RA) using digital camera images of bilateral dorsal hands, benchmarking its performance against the widely-used 2010 ACR/EULAR criteria as a clinical reference standard.
methodsThis pilot study included 170 participants (86 RA, 84 non-RA) who presented with joint symptoms at participating medical institutions. Digital images of both dorsal hands were captured under standardized conditions and processed using a deep learning-based background removal algorithm. A Swin Transformer-based model was developed and trained on these images. Model performance was evaluated using area under the receiver operating characteristics curve (AUROC), sensitivity, specificity, and calibration metrics. Gradient-Weighted Class Activation Mapping (Grad-CAM) was employed to visualize the model’s decision-making process.
resultsThe deep learning model achieved an AUROC of 0.870 (95% CI: 0.708–0.988), compared with 0.981 (95% CI: 0.953–1.010) for the ACR/EULAR criteria, with the difference not reaching statistical significance (p = 0.131). While demonstrating comparable sensitivity to the ACR/EULAR criteria, the model showed lower specificity, accuracy, and F1-score. Post-Platt scaling calibration analysis revealed good alignment with ideal calibration in the 0.4–0.6 probability range. Grad-CAM visualization confirmed that the model focused on clinically relevant joint regions, particularly the metacarpophalangeal and proximal interphalangeal joints.
conclusionOur deep learning-based approach for RA diagnosis using standard digital camera images demonstrated clinically viable performance, albeit with lower specificity than the ACR/EULAR criteria. This accessible screening tool could potentially expedite early RA detection, particularly in resource-limited settings. Larger multi-centre studies are needed to validate our findings and establish broader clinical applicability. CLINICAL TRIAL NUMBER: Not applicable.
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