Modelling the loading mechanics of anterior cruciate ligament.


Journal

Computer methods and programs in biomedicine
ISSN: 1872-7565
Titre abrégé: Comput Methods Programs Biomed
Pays: Ireland
ID NLM: 8506513

Informations de publication

Date de publication:
Feb 2020
Historique:
received: 24 07 2019
revised: 23 09 2019
accepted: 25 09 2019
pubmed: 8 11 2019
medline: 7 1 2021
entrez: 8 11 2019
Statut: ppublish

Résumé

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. 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. The total ACL force predicted by the 3D computational ACL force model was in good agreement with cadaveric data, as strong correlation (r 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.

Sections du résumé

BACKGROUND AND OBJECTIVES OBJECTIVE
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 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 RESULTS
The total ACL force predicted by the 3D computational ACL force model was in good agreement with cadaveric data, as strong correlation (r
CONCLUSIONS 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.

Identifiants

pubmed: 31698195
pii: S0169-2607(19)31207-6
doi: 10.1016/j.cmpb.2019.105098
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

105098

Informations de copyright

Copyright © 2019 Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare no competing interests.

Auteurs

Azadeh Nasseri (A)

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.

Hamid Khataee (H)

School of Mathematics and Physics, The University of Queensland, St. Lucia, Brisbane, Australia.

Adam L Bryant (AL)

Centre for Exercise, Health & Sports Medicine, University of Melbourne, Australia.

David G Lloyd (DG)

School of Allied Health Sciences, Griffith University, Australia; Griffith Centre of Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland, Griffith University, Australia.

David J Saxby (DJ)

School of Allied Health Sciences, Griffith University, Australia; Griffith Centre of Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland, Griffith University, Australia.

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Classifications MeSH