PIDD-MSK: Physics-Informed Data-Driven Musculoskeletal Modelling

肌肉骨骼模型是功能强大的计算模拟工具，可用于详细的生物力学分析，以估计内部运动变量，这些变量在体内不易测量，但临床医生对此很感兴趣。传统的基于物理的肌肉骨骼模型耗时长，运行时延高，而数据驱动的肌肉骨骼模型速度快，但不能反映潜在的物理机制。我的建议是克服这两种类型的肌肉骨骼模型的局限性，同时保留它们的优势，我的愿景是开发下一代基于物理的数据驱动肌肉骨骼模型，它可以无缝地将现有的基于物理的领域知识集成到数据驱动模型中。主要创新是1)将物理信息引入到数据驱动的肌肉骨骼模型中，通过创建反映底层物理机制的数据来克服其局限性；2)将深度学习引入到基于物理的肌肉骨骼模型中，以减少数据处理中的计算需求，提高实时应用的执行速度。本文将开发的模型应用于脑卒中患者上肢损伤定量评估作为案例研究。除了中风患者外，该项目的成果还可能使数百万患有多发性硬化症和脊髓损伤以及肌肉骨骼疾病的人受益。这个奖学金也将提高我的研究技能，加强我的学术形象，建立我的领导能力，扩大我在医疗保健机器学习/深度学习方面的合作联系，为我成为该领域的世界领先专家奠定基础，以便我将来可以做出科学贡献，并为欧洲产生社会和经济影响。

Musculoskeletal models are powerful, computational simulation tools for detailed biomechanical analysis to estimate internal movement variables, which cannot be readily measured in vivo but are of interest to clinicians. The traditional physics-based musculoskeletal models are time-consuming and with high running latency, while the data-driven musculoskeletal models are fast but cannot reflect the underlying physical mechanisms.This proposal is to overcome limitations associated with both types of musculoskeletal models while preserving their advantages, and my vision is to develop the next-generation physics-informed data-driven musculoskeletal models, which can seamlessly integrate the existing physics-based domain knowledge into the data-driven models. The main innovations are 1) to bring physics information into data-driven musculoskeletal models to overcome its limitations by creating data reflecting the underlying physical mechanisms, and 2) to bring deep learning into physics-based musculoskeletal models to reduce computational demands in data processing and improve execution speed for real-time applications. I will apply the developed model for quantitative upper extremity impairment assessment for stroke patients as the case study. In addition to stroke patients, the outcome of this project can also potentially benefit millions of people with conditions such as multiple sclerosis and spinal cord injury, as well as people with musculoskeletal conditions.This fellowship would also improve my research skills, strengthen my academic profile, build my leadership capability and broaden my collaboration links in machine learning/deep learning for healthcare, and it will lay the foundation for me to become a worldleading expert in this area so that I can make scientific contributions and generate societal and economic impacts for Europe in the future.