Azadeh Nasseri1, Hamid Khataee2, Adam L Bryant3, David G Lloyd4, David J Saxby4. 1. School of Allied Health Sciences, Griffith University, Australia; Griffith Centre of Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland, Griffith University, Australia. Electronic address: azadeh.nasseri@griffithuni.edu.au. 2. School of Mathematics and Physics, The University of Queensland, St. Lucia, Brisbane, Australia. 3. Centre for Exercise, Health & Sports Medicine, University of Melbourne, Australia. 4. School of Allied Health Sciences, Griffith University, Australia; Griffith Centre of Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland, Griffith University, Australia.
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 (r2 = 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.
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 (r2 = 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.
Authors: Sara Sadat Farshidfar; Joseph Cadman; Danny Deng; Richard Appleyard; Danè Dabirrahmani Journal: PLoS One Date: 2022-01-27 Impact factor: 3.240
Authors: Robert Csapo; Dieter Heinrich; Andrew D Vigotsky; Christian Marx; Shantanu Sinha; Christian Fink Journal: Diagnostics (Basel) Date: 2021-11-16