A computational musculoskeletal model for ACL injury risk analysis: Development and validation

Computational musculoskeletal models possess great potential to quantify biomechanics across joint, muscle, and ligament levels during dynamic movements — a capability essential for uncovering the underlying mechanisms of anterior cruciate ligament (ACL) injury. However, comprehensive frameworks that fully integrate these multi-level features remain scarce. In this study, we developed a novel computational musculoskeletal model by integrating a discrete-element knee model containing ligaments into a full-body musculoskeletal model featuring detailed trunk and lower-extremity musculature. The accuracy of estimated ligament and muscle geometries were verified against those of the original component models under identical prescribed motions, and the mechanical behavior of the knee ligaments was rigorously validated through a series of forward dynamics simulations. The validated model was then applied to simulate four high-risk movements in fifteen healthy participants, after which linear regression analysis was performed to quantify the associations among kinematic variables derived from inverse kinematics, muscle forces estimated using a personalized EMG-driven modeling approach, and ACL strain/force. The proposed model demonstrated robust knee joint stability across various loading conditions and flexion angles, while preserving accurate ligament and muscle geometries relative to the source models. Increased ACL strain during the landing phase was significantly associated with greater knee abduction and anterior tibial translation. Quadriceps, gastrocnemius, and adductor forces consistently exhibited ACL-loading effects across all tasks, whereas hamstring forces demonstrated a task-dependent relationship with ACL force. Ultimately, this model provides a powerful tool for identifying risk factors associated with ACL injury, facilitating the development and refinement of evidence-based prevention strategies.