Early prediction of joint space narrowing in rheumatoid arthritis using AI-quantified bilateral joint space asymmetry on hand radiography.
Authors
Affiliations (8)
Affiliations (8)
- Faculty of Health Sciences, Hokkaido University, North-12, West-5, Kita-ku, Sapporo, 060-0812, Japan.
- Graduate School of Health Sciences, Hokkaido University, Sapporo, Japan.
- Division of Rheumatology, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China.
- Global Center for Biomedical Science and Engineering, Hokkaido University, Sapporo, Japan.
- Research Center for Integrated Quantum Electronics, Hokkaido University, Sapporo, Japan.
- Department of Imaging and Interventional Radiology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, China.
- Department of Radiology, Phramongkutklao Hospital, Bangkok, Thailand.
- Faculty of Health Sciences, Hokkaido University, North-12, West-5, Kita-ku, Sapporo, 060-0812, Japan. [email protected].
Abstract
Joint space narrowing (JSN) in rheumatoid arthritis (RA) can progress even during clinical remission. Conventional imaging lacks sensitivity for detecting early risks. This study evaluates AI-based analysis of bilateral joint-space asymmetry on hand radiographs to detect future JSN progression in radiographically normal RA. Forty-six radiographically normal RA patients underwent bilateral hand radiography and high-resolution peripheral quantitative computed tomography (HR-pQCT) at baseline, 12, and 24 months. Five joint space parameters were extracted from HR-pQCT. Gaussian mixture modeling (GMM) stratified patients into progressive and non-progressive phenotypes using 24 month data. An AI pipeline was developed to measure bilateral joint space asymmetry from baseline X-rays using deep learning-based segmentation and registration. Predictive models were constructed using logistic regression and evaluated via ROC curves. GMM identified 17 progressive and 29 non-progressive patients. HR-pQCT-derived intra-joint space width inconsistency (JSW.Inc) increased significantly over time in progressors (p = 0.0337). At baseline, HR-pQCT-based JSW.Inc showed fair predictive power (AUC = 0.696). AI-derived asymmetry at PIP3 and MCP3 joints showed stronger baseline prediction (AUCs = 0.739 and 0.714, respectively). By month 12, predictive performance improved across models (AUCs up to 0.836 for HR-pQCT and 0.748 for AI-based metrics). AI-assisted analysis of bilateral joint space asymmetry from hand radiography may provide an accessible method for detecting radiographic JSN progression, with prognostic findings that should be interpreted as exploratory and hypothesis-generating. This method complements conventional imaging tools and may support future investigations into early risk stratification.