Integrating Musculoskeletal and Data-Driven Modeling to Understand the Biomechanical Sequelae of Syndesmotic Repair

项目总结 踝关节联合损伤在脚踝骨折和扭伤等骨科损伤中很常见。外科手术 修复踝关节粘连需要将腓骨固定在胫骨上。贫困患者的病因学研究 关节修复后的结果，如疼痛和骨关节炎，尚不清楚。中环 这项工作的假设假设是，联合修复破坏了整个下肢的生物力学。人类 由动物界仅有的两个目之一组成，具有专门的、完全活动的腓骨。腓骨运动 促进整个下肢的减震和稳定。当下肢形成一个 相互依赖的机械链，腓骨固定可能会破坏整个关节的生物力学和功能 从臀部到脚部的四肢。我们的长期目标是推动诊断和治疗范例的发展 通过更好地了解活动腓骨的生物力学作用，进一步了解关节损伤。 这项工作的目的是通过比较来描述腓骨的生物力学和相关后遗症。 对健康、活动的腓骨和手术固定的腓骨的受试者进行检查。我们将首先评估 健康人群与踝关节手术修复患者的生物力学差异 联结(目标1)。我们将记录运动过程中的动作捕捉、力量和肌电数据， 功能性的和运动性的任务。使用我们的实验数据，我们将利用肌肉骨骼模拟来 评估腓骨活动度对后足关节反作用力的影响(目标2)。最后，我们将使用可解释的 机器学习从生物力学数据预测关节损伤状态并识别高影响预测因子 (目标3)。通过创新的实验和计算方法相结合，我们将提高生物力学 理解腓骨固定在关节突修复术中的意义。了解什么是生物力学 与联合修复相关的差异和功能缺陷将为新的手术提供证据 和康复方案。确定哪些生物力学变化是突触的高影响预测因素 修复将为开发数据驱动的联合损伤诊断和预后奠定基础。 通过这项建议，申请者将获得关于实验生物力学的独特组合的培训。 方法(例如，运动捕捉、表面和肌肉内肌电(EMG)、超声成像)和 量化数据驱动的方法(例如，肌肉骨骼模拟、机器学习)。伊利诺伊大学 佛罗里达州将为申请者提供跨学科研究的绝佳机会，杰出的导师， 以及一流的训练环境。此外，该大学的人工智能倡议提供了无与伦比的 有机会发展世界级的人工智能专业知识。这些经验将增强申请者的技术和 专业技能，为成功的学术研究人员职业生涯提供所需的培训。

PROJECT SUMMARY Injury to the ankle syndesmosis is common in orthopaedic injuries like ankle fractures and sprains. Surgical repair of the ankle syndesmosis involves rigid fixation of the fibula to the tibia. The etiology of poor patient outcomes following syndesmotic repair, such as pain and osteoarthritis, is not well understood. The central hypothesis of this work posits that syndesmotic repair disrupts the biomechanics of the entire lower limb. Humans comprise one of only two orders in the Animal Kingdom with specialized, fully-mobile fibulae. Fibular motion facilitates shock absorption and stabilization throughout the lower limb. As the lower limb forms an interdependent, mechanical chain, fibular fixation could disrupt both the biomechanics and function of the entire lower limb from the hip to the foot. Our long-term goal is to advance diagnostic and treatment paradigms for syndesmotic injury by better understanding the biomechanical role of the mobile fibula. The objective of this work is to characterize fibular biomechanics and associated sequelae through comparative examination of subjects with healthy, mobile fibulae and surgically immobilized fibulae. We will first evaluate biomechanical differences between healthy individuals and individuals with surgically repaired ankle syndesmoses (Aim 1). We will record motion capture, force, and electromyography data during locomotion, functional, and athletic tasks. Using our experimental data, we will leverage musculoskeletal simulations to assess the effect of fibular mobility on hindfoot joint reaction forces (Aim 2). Finally, we will use explainable machine learning to predict syndesmotic injury state from biomechanical data and identify high-impact predictors (Aim 3). By combining innovative experimental and computational methods, we will improve the biomechanistic understanding of implications of fibular fixation during syndesmotic repair. Understanding what biomechanical differences and functional deficits are associated with syndesmotic repair will provide evidence for new surgical and rehabilitative protocols. Identifying which biomechanical changes are high impact predictors of syndesmotic repair will lay the groundwork to develop data-driven diagnostics and prognostics for syndesmotic injury. Through this proposal, the applicant will obtain training on a unique combination of experimental biomechanics methods (e.g., motion capture, surface and intramuscular electromyography (EMG), ultrasound imaging) and quantitative data-driven approaches (e.g., musculoskeletal simulation, machine learning). The University of Florida will provide the applicant outstanding opportunities for interdisciplinary research, exceptional mentors, and a phenomenal training environment. Further, the University’s AI Initiative provides an unparalleled opportunity to develop world-class AI expertise. These experiences will enhance the applicant’s technical and professional skills, providing the training needed for a successful career as an academic researcher.