Frontiers in medicine · 2026
Deep learning-based multiclass classification for the diagnosis of hand arthritis on digital radiography: a multicenter study.
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Deng Chao, Yan Dan, Tang Cong, Deng Xuemei, Li Chuanyu, Chen Xikui, Deng Lihua, Wu Zhe
Аннотация
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.
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.