标题： 骨关节炎和膝关节置换的qMRI生物标志物的膝关节数字孪生基础 显示英文标题

标题： Foundations of a Knee Joint Digital Twin from qMRI Biomarkers for Osteoarthritis and Knee Replacement

摘要： 这项研究构成了膝关节数字孪生系统的基础，利用先进的定量磁共振成像（qMRI）和机器学习来推进骨关节炎（OA）管理和膝关节置换（KR）预测的精准健康。 我们将基于深度学习的膝关节结构分割与降维相结合，创建了影像生物标志物的嵌入特征空间。 通过横断面队列分析和统计建模，我们确定了特定的生物标志物，包括软骨厚度和内侧半月板形状的变化，这些变化与OA发病率和KR结果显著相关。 将这些发现整合到一个综合框架中代表了向个性化膝关节数字孪生迈出的重要一步，这可能会增强治疗策略，并为风湿病学护理中的临床决策提供信息。 这种多功能且可靠的基础设施有可能扩展到精准健康中的更广泛的临床应用。 显示英文摘要

摘要： 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.