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Purpose <p>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.</p> Materials and methods <p>Forty-six radiographically normal RA patients underwent bilateral hand radiography and high-resolution peripheral quantitative computed tomography (HR-pQCT) at baseline, 12, and 24&#xa0;months. Five joint space parameters were extracted from HR-pQCT. Gaussian mixture modeling (GMM) stratified patients into progressive and non-progressive phenotypes using 24&#xa0;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.</p> Results <p>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).</p> Conclusions <p>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.</p>

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Early prediction of joint space narrowing in rheumatoid arthritis using AI-quantified bilateral joint space asymmetry on hand radiography

  • 期刊论文

  • 发表日期：

    2026年7月1日

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  • Bingqian Lu,
  • Haolin Wang,
  • Wanxuan Fang,
  • Sze Lok Lau,
  • Isaac Cheng,
  • Kenneth Sutherland,
  • Masayuki Ikebe,
  • Lai-Shan Tam,
  • James F. Griffith,
  • Tunlada Wiriyanukhroh,
  • Zheyu Ye,
  • Tamotsu Kamishima

摘要

Purpose

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.

Materials and methods

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.

Results

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).

Conclusions

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.