Modelling the loading mechanics of anterior cruciate ligament. (February 2020) - Nasseri, Azadeh
Highlights: Several computational models have studied the loading of the anterior cruciate ligament (ACL), essential for the knee stability. Earlier models are not validated, validated against a limited sample data, shown non physiological ACL loading patterns, or limited by the loading magnitudes. A novel computational model is proposed which predicts ACL force in close agreement with experiments, showing strong correlations, minimal bias, and narrow limits of agreement. The model is then combined with a neuromusculoskeletal model of lower limb to estimate in vivo ACL forces, and concludes that the ACL is primarily loaded through the sagittal plane, mainly due to muscle loading. Model is of low computational cost and requires minimal mechanical information about the knee articular tissues, making it widely accessible and useful for developing and testing ACL knee injury prevention programs. Abstract: Background and Objectives: The anterior cruciate ligament (ACL) plays a crucial role in knee stability and is the most commonly injured knee ligament. Although ACL loading patterns have been investigated previously, the interactions between knee loadings transmitted to ACL remain elusive. Understanding the loading mechanism of ACL during dynamic tasks is essential to prevent ACL injuries. Therefore, we propose a computational model that predicts the force applied to ACL in response to knee loading in three planes of motion. Methods: First, a three-dimensional (3D) computational model was developed and validated using available cadaveric experimental data to predict ACL force. This 3D model was then combined with a neuromusculoskeletal model of lower limb and used to estimate in vivo ACL forces during a standardised drop-landing task. The neuromusculoskeletal model utilised movement data collected from female participants during a dynamic task and calculated lower limb joint kinematics and kinetics, as well as muscle forces. Results: The total ACL force predicted by the 3D computational ACL force model was in good agreement with cadaveric data, as strong correlation ( r 2 = 0.96 and P < 0.001), minimal bias, and narrow limits of agreement were observed. The combined model further illustrated that the ACL is primarily loaded through the sagittal plane, mainly due to muscle loading. Conclusions: The proposed computational model is the first validated model that can provide an accessible tool to develop and test knee ACL injury prevention programs for people with normal ACL. This method can be extended to study the abnormal ACL upon the availability of relevant experimental data.