Injury risk functions for the four primary knee ligaments

Frontiers in Bioengineering and Biotechnology

Bioengineering and Biotechnology

Johan Knälmann¹  Reimert Sjöblom¹  Jiota Nusia²  Jia-Cheng Xu³  Svein Kleiven⁴ 

[1] Department of Strength and Crash Analysis, Scania CV AB, Södertälje, Sweden;Department of Traffic Safety and Traffic Systems, The Swedish National Road and Transport Research Institute (VTI), Stockholm, Sweden;Department of Traffic Safety and Traffic Systems, The Swedish National Road and Transport Research Institute (VTI), Stockholm, Sweden;Division of Neuronic Engineering, KTH Royal Institute of Technology, Stockholm, Sweden;Division of Neuronic Engineering, KTH Royal Institute of Technology, Stockholm, Sweden;

关键词: injury risk function;    knee ligaments;    cruciate ligament;    collateral ligament;    failure strain;    human body model;    cumulative distribution function;   

DOI  :  10.3389/fbioe.2023.1228922

 received in 2023-05-25, accepted in 2023-09-11,  发布年份 2023

来源: Frontiers

【 摘 要 】

The purpose of this study was to develop injury risk functions (IRFs) for the anterior and posterior cruciate ligaments (ACL and PCL, respectively) and the medial and lateral collateral ligaments (MCL and LCL, respectively) in the knee joint. The IRFs were based on post-mortem human subjects (PMHSs). Available specimen-specific failure strains were supplemented with statistically generated failure strains (virtual values) to accommodate for unprovided detailed experimental data in the literature. The virtual values were derived from the reported mean and standard deviation in the experimental studies. All virtual and specimen-specific values were thereafter categorized into groups of static and dynamic rates, respectively, and tested for the best fitting theoretical distribution to derive a ligament-specific IRF. A total of 10 IRFs were derived (three for ACL, two for PCL, two for MCL, and three for LCL). ACL, MCL, and LCL received IRFs in both dynamic and static tensile rates, while a sufficient dataset was achieved only for dynamic rates of the PCL. The log-logistic and Weibull distributions had the best fit (p-values: >0.9, RMSE: 2.3%–4.7%) to the empirical datasets for all the ligaments. These IRFs are, to the best of the authors’ knowledge, the first attempt to generate injury prediction tools based on PMHS data for the four knee ligaments. The study has summarized all the relevant literature on PHMS experimental tensile tests on the knee ligaments and utilized the available empirical data to create the IRFs. Future improvements require upcoming experiments to provide comparable testing and strain measurements. Furthermore, emphasis on a clear definition of failure and transparent reporting of each specimen-specific result is necessary.

【 授权许可】

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Copyright © 2023 Nusia, Xu, Knälmann, Sjöblom and Kleiven.