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

整合肌肉骨骼和数据驱动建模以了解韧带联合修复的生物力学后遗症

• 批准号: 10751099
• 负责人: Chloe Baratta
• 金额: $ 4.24万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-16 至 2026-08-15
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10751099
• 关键词: Affect; Animals; Ankle; Ankle Fracture; Articular Range of Motion; Articulation; Athletic; Biomechanics; Cadaver; Classification; Compensation; Complex; Computational Technique; Computing Methodologies; Data; Degenerative polyarthritis; Development; Diagnosis; Diagnostic; Electromyography; Environment; Etiology; Femur; Florida; Goals; Hip region structure; Human; Image; Immobilization; Individual; Injury; Interdisciplinary Study; Intramuscular; Intuition; Joints; Kinetics; Limb structure; Locomotion; Lower Extremity; Machine Learning; Measures; Mechanics; Mentors; Methods; Modeling; Motion; Movement; Muscle; Musculoskeletal; Oncology; Operative Surgical Procedures; Orthopedics; Outcome; Pain; Patient-Focused Outcomes; Patients; Performance; Play; Population; Protocols documentation; Reaction; Rehabilitation therapy; Research; Research Personnel; Role; Shock; Skin; Specificity; Sprain; Subtalar joint structure; Surface; Task Performances; Techniques; Training; Ultrasonography; Universities; Weight-Bearing state; Work; absorption; acute symptom; arm; bone; career; comparative; data-driven model; deep learning; disability; experience; fibula; foot; functional disability; functional outcomes; improved; in vivo; innovation; kinematics; limb bone; machine learning method; machine learning model; model building; personalized diagnostics; personalized medicine; predictive modeling; prognostic; repaired; sample fixation; simulation; skills; tibia

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.

项目总结