Given that hip osteoarthritis is associated with the mechanical environment surrounding the hip joint, it is essential to develop a high-precision musculoskeletal analysis that accurately reflects the muscle mechanical properties of individual patients. This study aimed to evaluate the effects of patient-specific muscle strength, predicted by artificial intelligence (AI), on musculoskeletal analysis. We employed a statistical model of the musculoskeletal system to extract the bone and muscle morphology of the full lower limb from partial medical images. Subsequently, we developed a computational workflow to incorporate muscle strength, estimated from individual muscle shape measurements, into musculoskeletal modelling. We also compared the muscle forces derived from the musculoskeletal analysis with and without using our proposed workflow. The estimated muscle forces were consistent with the general characteristics of hip osteoarthritis, and the joint reaction forces were altered. These findings suggest that our technique for predicting muscle geometry using AI enhances subject-specific musculoskeletal simulations of hip osteoarthritis.
OBAYASHI et al. (Wed,) studied this question.