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

项目摘要 踝关节韧带损伤在骨科损伤中很常见，如踝关节骨折和扭伤。手术 踝关节韧带联合的修复涉及腓骨与胫骨的刚性固定。贫困患者的病因 韧带联合修复后的结果，如疼痛和骨关节炎，还没有得到很好的理解。中央 这项工作的假设假定，韧带联合修复破坏了整个下肢的生物力学。人类 是动物王国中仅有的两个拥有专门的、完全活动的腓骨的目之一。腓骨运动 有利于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生物力学和功能， 从臀部到脚的下肢。我们的长期目标是推进诊断和治疗模式， 通过更好地了解移动的腓骨的生物力学作用， 这项工作的目的是通过比较腓骨生物力学和相关后遗症的特点 检查具有健康的、移动的腓骨和手术固定的腓骨的受试者。我们将首先评估 踝关节手术修复前后的生物力学差异 联合韧带（目的1）。我们将记录运动过程中的动作捕捉，力和肌电图数据， 功能和运动任务。使用我们的实验数据，我们将利用肌肉骨骼模拟， 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 机器学习从生物力学数据预测下胫联合损伤状态并识别高影响力预测因子 (Aim 3）。通过结合创新的实验和计算方法，我们将改善生物力学 了解下胫腓联合修复中腓骨固定的意义。了解生物力学 与下胫联合修复相关的差异和功能缺陷将为新的外科手术提供证据。 和康复方案确定哪些生物力学变化是下胫联合的高影响预测因子 修复将为开发数据驱动的诊断和韧带联合损伤的诊断奠定基础。 通过这项建议，申请人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、表面和肌内肌电图（EMG）、超声成像）和 定量数据驱动的方法（例如，肌肉骨骼模拟、机器学习）。大学 佛罗里达将为申请人提供跨学科研究，杰出导师， 和非凡的训练环境。此外，大学的人工智能计划提供了一个无与伦比的 发展世界级AI专业知识的机会。这些经验将提高申请人的技术和 专业技能，提供成功的职业生涯作为一个学术研究所需的培训。

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.