Deep learning-based multiclass classification for the diagnosis of hand arthritis on digital radiography: a multicenter study

Abstract

Background To develop and independently validate a deep learning framework for multiclass differentiation of osteoarthritis (OA), rheumatoid arthritis (RA), gouty arthritis (GA), and normal controls on hand digital radiographs. Methods This retrospective multicenter study included 1,179 hand radiographs from 520 participants at four institutions. A three-center development cohort comprised 1,001 radiographs from 431 participants and was evaluated using patient-level stratified five-fold cross-validation. ResNet50, ResNet101, DenseNet121, and Vision Transformer (ViT) were trained using an identical pipeline. The internally best model, ViT, was retrained on the complete development cohort and evaluated once on an independent fourth-center cohort of 178 radiographs from 89 participants that was held out from training and model selection. Results ViT achieved the numerically highest internal performance, with an accuracy of 0.937 ± 0.029 and a macro-F1 score of 0.937 ± 0.029. On independent external validation, ViT correctly classified 133 of 178 radiographs, with accuracy of 0.747, macro-precision of 0.756, macro-recall of 0.747, macro-specificity of 0.916, macro-F1 of 0.744, and macro-AUC of 0.917. External class-wise AUCs were 0.948 for RA, 0.873 for OA, 0.888 for GA, and 0.959 for normal controls. Conclusion The transformer-based model showed strong internal performance and retained useful discriminative ability in an independent external cohort; however, the reduction in external accuracy and macro-F1 indicated limited cross-center transferability. These findings support the feasibility of the proposed approach while emphasizing the need for broader prospective validation and reader-performance studies before clinical deployment.