Automated pipeline for creating personalized biomechanical knee models and computing personalized cartilage pressures during gait

Motivation: Automated analysis of MRI and biomechanics data can provide personalized information about cartilage pressures. Goal(s): Our goal was to develop an automated pipeline to create a personalized biomechanical knee joint model from MRI data, to simulate personalized knee mechanics during gait in comparison to knee mechanics of a generic knee joint geometry. Approach: Bone and cartilage geometry was automatically segmented from knee MRI scans via deep learning. Gait simulations were performed on musculoskeletal models with personalized and generic knee models. Results: Personalizing knee joint geometries affected cartilage pressure distributions in the joint but maintained peak cartilage pressures and contact forces. Impact: Biomechanical models personalized with MRI data enable understanding of how bone geometry influences cartilage pressures during gait, which may lead to better tailoring and evaluation of interventions.