Personalized musculoskeletal models for assessment of dynamic patellofemoral function

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The articulation between the patella and femur in the knee is a common site of musculoskeletal injury and dysfunction as a result of athletic or occupational activities. The function and dysfunction of this joint are still not well understood due to the difficulty of measuring the movement of the patella beneath the skin using traditional methods, such as optical motion capture which uses skin mounted reflective markers. Computational models provide an attractive option for overcoming these challenges as they enable estimation of measures that are difficult or impossible to obtain in living humans and provide a means to isolate the impact of individual factors on joint function. The long-term goal of my research program is to use personalized musculoskeletal models to improve our understanding of the influence of individual factors on dynamic patellofemoral function and dysfunction. Currently, generating personalized musculoskeletal models is a time- and cost-intensive process and their use is confined to the laboratory setting, thus limiting the number of participants and types of activities that can be studied. Therefore, this proposal is focused on the development and validation of methods to improve the accessibility and applicability of personalized musculoskeletal models. This will be achieved through two specific aims: 1) develop and validate methods to generate personalized models of knee geometry using machine learning with sparse imaging and measurement data, and 2) enable the use of musculoskeletal models `in the wild' by integrating video-based markerless motion capture and estimations of external forces within computational simulation frameworks. It is increasingly being recognized that personalized biomechanical models are more accurate due to the variable nature of the musculoskeletal system. This research will simplify the generation of personalized musculoskeletal models and enable their use for a wider range of applications. This would facilitate the study of much greater numbers of people performing a variety of activities to better capture the variability that exists in patellofemoral anatomy and would better represent those from diverse groups who are currently not well captured by generic models. This is vital for improving our understanding of patellofemoral function and dysfunction because of the individual nature of this joint and the fact that injuries often arise in dynamic athletic and occupational activities. The ability to generate large datasets would also allow for the use of machine learning and data analytics to reveal patterns and phenotypes in patellofemoral function. These types of advances could also form the basis for the development of tools that can be implemented in clinics to aid in diagnoses and the planning of patient-specific interventions. Finally, participation in this research will provide HQP with experiences and skills that will be valuable whether they pursue careers in industry or academia.