Detecting inflammatory arthritis in hand smartphone photographs: development and validation of a computer vision...

Computer vision can classify inflammatory arthritis on smartphone photographs. We aimed to train, validate and judiciously choose a model for detecting hand synovitis from standardized smartphone photographs in a real-world rheumatology outpatient population.

A dataset of 2296 hand photos from 1112 patients attending rheumatology clinics in India was partitioned at the patient level into training (70%), validation (15%) and test (15%) sets. Two deep learning architectures (ConvNeXt V2 and EfficientNetV2) and their weighted ensemble were trained against a ground truth of specialist-detected synovitis and compared using area under the receiver operating characteristic curve (AUROC). In the chosen model, 95% confidence intervals were obtained via patient-level bootstrap in the independent test set. Prespecified subgroup analyses examined model performance by deformity status, age and sex.

ConvNeXt V2 outperformed EfficientNetV2 (validation AUROC 0.856 vs 0.831). The ensemble achieved the highest validation AUROC (0.864, α = 0.24), with modest incremental gain over ConvNeXt. On the independent test set, ConvNeXt achieved an AUROC of 0.852 (95% CI: 0.802, 0.896). At the fixed operating threshold, test accuracy was 0.79 (95% CI: 0.75, 0.83), sensitivity 0.76 (95% CI: 0.67, 0.85) and specificity 0.80 (95% CI: 0.75, 0.84). Model performance remained stable across all prespecified subgroups, including patients with hand deformities.

A computer vision model trained on standardized smartphone photographs can detect hand synovitis in routine clinical populations including in those with deformities. This validated model on a large, prospectively assembled dataset represent an important step toward scalable decision support in non-specialist settings and reducing diagnostic delay in inflammatory arthritis.