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已发表论文
手部 X 光片中类风湿性关节炎的深度学习分类：可解释性见解及网络应用
Authors Cai K, Dou D , Deng G, Zhan Y, Huang H , Feng Z
Received 14 June 2025
Accepted for publication 30 October 2025
Published 19 November 2025 Volume 2025:14 Pages 1333—1345
DOI https://doi.org/10.2147/ITT.S547159
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Professor Jadwiga Jablonska
Kanglin Cai,1,2,* Dengfeng Dou,3,* Guibing Deng,4,* Yunzhen Zhan,5 Huilian Huang,1 Zhitao Feng2,6
1The Second People’s Hospital Affiliated to Three Gorges University / Yichang Second People’s Hospital, Yichang, Hubei, 443000, People’s Republic of China; 2Third-Grade Pharmacological Laboratory on Chinese Medicine Approved by State Administration of Traditional Chinese Medicine, College of Medicine and Health Science, China Three Gorges University, Yichang, Hubei, 443002, People’s Republic of China; 3Independent Cardiovascular Research Lab, Chinese Institutes for Medical Research, Capital Medical University, Beijing, 100069, People’s Republic of China; 4The First College of Clinical Medical Sciences, China Three Gorges University, Yichang, Hubei, 443003, People’s Republic of China; 5College of Engineering, China Pharmaceutical University, Nanjing, Jiangsu, 211198, People’s Republic of China; 6Institute of Rheumatology, the First College of Clinical Medical Sciences, China Three Gorges University, Yichang, Hubei, 443003, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Zhitao Feng, College of Medicine and Health Science, China Three Gorges University, Yichang, Hubei, 443002, People’s Republic of China, Tel +86-0717-6396558, Email fengzhitao2008@126.com Huilian Huang, The Second People’s Hospital Affiliated to Three Gorges University, Yichang Second People’s Hospital, Yichang, Hubei, 443000, People’s Republic of China, Tel +86-13607207438, Email ann19830418@163.com
Purpose: To establish an interpretable deep learning framework for automated classification of rheumatoid arthritis (RA) in hand radiographs, with emphasis on elucidating model decision-making patterns and enabling clinical translation through web-based deployment.
Patients and Methods: A retrospective multicenter study analyzed 1,655 hand radiographs (809 RA patients, including early RA cases, and 846 healthy controls). Enhanced data (random rotation, brightness/contrast adjustment) was applied to the collected X-ray images to improve the model’s generalization ability and performance. Subsequently, A lightweight Visual Geometry Group (VGG)-8 convolutional neural network was trained and validated using processed hand X-ray images. This model has the ability to distinguish RA patients from healthy controls. The interpretability of the model was systematically evaluated using both Gradient-weighted Class Activation Mapping (Grad-CAM) and Shapley Additive Explanations (SHAP). Finally, a web application was developed using Streamlit that supports JPEG input, helps to address the clinical practicality of the model.
Results: For distinguishing RA patients from healthy individuals, the classifier achieved excellent training performance (AUC=0.99, accuracy=0.94) and generalizable testing metrics (AUC=0.81, accuracy=0.74). Specifically, the model was successfully constructed and demonstrated good performance in external validation. Interpretability analysis revealed areas of pathological significance, with Grad CAM heatmaps highlighting structural abnormalities (joint space stenosis, bone erosion, trabecular structural changes), and SHAP values analysis identifying metacarpophalangeal and wrist joints as key predictive features. A web application developed using Python and Streamlit framework can assist in the diagnosis of RA hand X-ray images in clinical practice.
Conclusion: This work advances clinical diagnosis, including early RA patients, by integrating deep learning with interpretable decision paths in hand radiographic analysis, while helping clinicians to use the model more proficiently. The framework provides both diagnostic assistance and educational insights into RA radiographic markers.
Keywords: radiograph interpretation, rheumatoid arthritis, visual geometry group, interpretability analysis, translational medicine
1The Second People’s Hospital Affiliated to Three Gorges University / Yichang Second People’s Hospital, Yichang, Hubei, 443000, People’s Republic of China; 2Third-Grade Pharmacological Laboratory on Chinese Medicine Approved by State Administration of Traditional Chinese Medicine, College of Medicine and Health Science, China Three Gorges University, Yichang, Hubei, 443002, People’s Republic of China; 3Independent Cardiovascular Research Lab, Chinese Institutes for Medical Research, Capital Medical University, Beijing, 100069, People’s Republic of China; 4The First College of Clinical Medical Sciences, China Three Gorges University, Yichang, Hubei, 443003, People’s Republic of China; 5College of Engineering, China Pharmaceutical University, Nanjing, Jiangsu, 211198, People’s Republic of China; 6Institute of Rheumatology, the First College of Clinical Medical Sciences, China Three Gorges University, Yichang, Hubei, 443003, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Zhitao Feng, College of Medicine and Health Science, China Three Gorges University, Yichang, Hubei, 443002, People’s Republic of China, Tel +86-0717-6396558, Email fengzhitao2008@126.com Huilian Huang, The Second People’s Hospital Affiliated to Three Gorges University, Yichang Second People’s Hospital, Yichang, Hubei, 443000, People’s Republic of China, Tel +86-13607207438, Email ann19830418@163.com
Purpose: To establish an interpretable deep learning framework for automated classification of rheumatoid arthritis (RA) in hand radiographs, with emphasis on elucidating model decision-making patterns and enabling clinical translation through web-based deployment.
Patients and Methods: A retrospective multicenter study analyzed 1,655 hand radiographs (809 RA patients, including early RA cases, and 846 healthy controls). Enhanced data (random rotation, brightness/contrast adjustment) was applied to the collected X-ray images to improve the model’s generalization ability and performance. Subsequently, A lightweight Visual Geometry Group (VGG)-8 convolutional neural network was trained and validated using processed hand X-ray images. This model has the ability to distinguish RA patients from healthy controls. The interpretability of the model was systematically evaluated using both Gradient-weighted Class Activation Mapping (Grad-CAM) and Shapley Additive Explanations (SHAP). Finally, a web application was developed using Streamlit that supports JPEG input, helps to address the clinical practicality of the model.
Results: For distinguishing RA patients from healthy individuals, the classifier achieved excellent training performance (AUC=0.99, accuracy=0.94) and generalizable testing metrics (AUC=0.81, accuracy=0.74). Specifically, the model was successfully constructed and demonstrated good performance in external validation. Interpretability analysis revealed areas of pathological significance, with Grad CAM heatmaps highlighting structural abnormalities (joint space stenosis, bone erosion, trabecular structural changes), and SHAP values analysis identifying metacarpophalangeal and wrist joints as key predictive features. A web application developed using Python and Streamlit framework can assist in the diagnosis of RA hand X-ray images in clinical practice.
Conclusion: This work advances clinical diagnosis, including early RA patients, by integrating deep learning with interpretable decision paths in hand radiographic analysis, while helping clinicians to use the model more proficiently. The framework provides both diagnostic assistance and educational insights into RA radiographic markers.
Keywords: radiograph interpretation, rheumatoid arthritis, visual geometry group, interpretability analysis, translational medicine