Predictions of Anterior Cruciate Ligament Dynamics From Subject-Specific Musculoskeletal Models and Dynamic Biplane Radiography

Abstract

Abstract In vivo knee ligament forces are important to consider for informing rehabilitation or clinical interventions. However, they are difficult to directly measure during functional activities. Musculoskeletal models and simulations have become the primary methods by which to estimate in vivo ligament loading. Previous estimates of anterior cruciate ligament (ACL) forces range widely, suggesting that individualized anatomy may have an impact on these predictions. Using ten subject-specific (SS) lower limb musculoskeletal models, which include individualized musculoskeletal geometry, muscle architecture, and six degree-of-freedom knee joint kinematics from dynamic biplane radiography (DBR), this study provides SS estimates of ACL force (anteromedial-aACL; and posterolateral-pACL bundles) during the full gait cycle of treadmill walking. These forces are compared to estimates from scaled-generic (SG) musculoskeletal models to assess the effect of musculoskeletal knee joint anatomy on predicted forces and the benefit of SS modeling in this context. On average, the SS models demonstrated a double force peak during stance (0.39–0.43 xBW per bundle), while only a single force peak during stance was observed in the SG aACL. No significant differences were observed between continuous SG and SS ACL forces; however, root mean-squared differences between SS and SG predictions ranged from 0.08 xBW to 0.27 xBW, suggesting SG models do not reliably reflect forces predicted by SS models. Force predictions were also found to be highly sensitive to ligament resting length, with ±10% variations resulting in force differences of up to 84%. Overall, this study demonstrates the sensitivity of ACL force predictions to SS anatomy, specifically musculoskeletal joint geometry and ligament resting lengths, as well as the feasibility for generating SS musculoskeletal models for a group of subjects to predict in vivo tissue loading during functional activities.