用qMRI和机器学习构建膝关节数字孪生，预测骨关节炎与置换风险。
Foundations of a Knee Joint Digital Twin from qMRI Biomarkers for Osteoarthritis and Knee Replacement
- 通过深度学习分割与降维，提取膝关节影像生物标志物。
- 发现软骨厚度与内侧半月板形态变化显著关联骨关节炎和置换结果。
- 为个性化治疗与临床决策提供可扩展的精准医疗框架。
本研究基于定量MRI（qMRI）与机器学习，构建膝关节数字孪生系统的基础框架，推动骨关节炎（OA）管理与膝关节置换（KR）预测的精准医疗发展。通过深度学习实现膝关节结构分割，并结合降维技术生成影像生物标志物嵌入空间。基于横断面队列分析与统计建模，识别出软骨厚度变化及内侧半月板形状特征等关键生物标志物，其与OA发生率及KR结局显著相关。将这些发现整合至综合框架中，标志着向个性化膝关节数字孪生迈出了重要一步，有望提升治疗策略并支持风湿病学临床决策。该通用且可靠的基础设施具备拓展至更广泛精准医疗应用的潜力。
原文摘要 · Abstract (English)
This study forms the basis of a digital twin system of the knee joint, using advanced quantitative MRI (qMRI) and machine learning to advance precision health in osteoarthritis (OA) management and knee replacement (KR) prediction. We combined deep learning-based segmentation of knee joint structures with dimensionality reduction to create an embedded feature space of imaging biomarkers. Through cross-sectional cohort analysis and statistical modeling, we identified specific biomarkers, including variations in cartilage thickness and medial meniscus shape, that are significantly associated with OA incidence and KR outcomes. Integrating these findings into a comprehensive framework represents a considerable step toward personalized knee-joint digital twins, which could enhance therapeutic strategies and inform clinical decision-making in rheumatological care. This versatile and reliable infrastructure has the potential to be extended to broader clinical applications in precision health.
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