构建多尺度肌肉骨骼数字孪生，实现个性化诊疗。
Framework of a multiscale data-driven DT of the musculoskeletal system
- 融合运动捕捉、超声等多源数据建模
- 精准提取脊柱运动与肌功能动态特征
- 适合临床康复与生物力学研究者使用
肌肉骨骼疾病是全球致残的主要原因，亟需先进诊断与治疗工具实现个性化评估。本文提出肌肉骨骼数字孪生（MS-DT）框架，整合多尺度生物力学数据与计算模型，构建患者特异性的肌肉骨骼系统高保真表示。通过融合运动捕捉、超声成像、肌电图与医学影像，MS-DT可分析脊柱运动学、姿势与肌肉功能。交互式可视化平台为临床与研究人员提供直观界面，用于探索生物力学参数并追踪个体变化。结果表明，该框架能有效提取精确的运动学与动力学组织特征，为脊柱生物力学监测与康复提供全面工具。该系统支持高保真建模与实时可视化，显著提升个性化诊断与干预规划能力。
原文摘要 · Abstract (English)
Musculoskeletal disorders (MSDs) are a leading cause of disability worldwide, requiring advanced diagnostic and therapeutic tools for personalised assessment and treatment. Effective management of MSDs involves the interaction of heterogeneous data sources, making the Digital Twin (DT) paradigm a valuable option. This paper introduces the Musculoskeletal Digital Twin (MS-DT), a novel framework that integrates multiscale biomechanical data with computational modelling to create a detailed, patient-specific representation of the musculoskeletal system. By combining motion capture, ultrasound imaging, electromyography, and medical imaging, the MS-DT enables the analysis of spinal kinematics, posture, and muscle function. An interactive visualisation platform provides clinicians and researchers with an intuitive interface for exploring biomechanical parameters and tracking patient-specific changes. Results demonstrate the effectiveness of MS-DT in extracting precise kinematic and dynamic tissue features, offering a comprehensive tool for monitoring spine biomechanics and rehabilitation. This framework provides high-fidelity modelling and real-time visualization to improve patient-specific diagnosis and intervention planning.
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