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

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.

Grant Number: 5F31AR083286-02
NIH Institute/Center: NIH
Principal Investigator: Chloe Baratta