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. 2022 May 26:10:841882.
doi: 10.3389/fbioe.2022.841882. eCollection 2022.

A Parameter Sensitivity Analysis on Multiple Finite Element Knee Joint Models

Nynke B Rooks  1 , Thor F Besier  1   2 , Marco T Y Schneider  1
Affiliations

A Parameter Sensitivity Analysis on Multiple Finite Element Knee Joint Models

Nynke B Rooks et al. Front Bioeng Biotechnol. .

Abstract

The reproducibility of computational knee joint modeling is questionable, with models varying depending on the modeling team. The influence of model variations on simulation outcomes should be investigated, since knowing the sensitivity of the model outcomes to model parameters could help determine which parameters to calibrate and which parameters could potentially be standardized, improving model reproducibility. Previous sensitivity analyses on finite element knee joint models have typically used one model, with a few parameters and ligaments represented as line segments. In this study, a parameter sensitivity analysis was performed using multiple finite element knee joint models with continuum ligament representations. Four previously developed and calibrated models of the tibiofemoral joint were used. Parameters of the ligament and meniscus material models, the cartilage contact formulation, the simulation control and the rigid cylindrical joints were studied. Varus-valgus simulations were performed, changing one parameter at a time. The sensitivity on model convergence, valgus kinematics, articulating cartilage contact pressure and contact pressure location were investigated. A scoring system was defined to categorize the parameters as having a "large," "medium" or "small" influence on model output. Model outcomes were sensitive to the ligament prestretch factor, Young's modulus and attachment condition parameters. Changes in the meniscus horn stiffness had a "small" influence. Of the cartilage contact parameters, the penalty factor and Augmented Lagrangian setting had a "large" influence on the cartilage contact pressure. In the rigid cylindrical joint, the largest influence on the outcome parameters was found by the moment penalty parameter, which caused convergence issues. The force penalty and gap tolerance had a "small" influence at most. For the majority of parameters, the sensitivity was model-dependent. For example, only two models showed convergence issues when changing the Quasi-Newton update method. Due to the sensitivity of the model parameters being model-specific, the sensitivity of the parameters found in one model cannot be assumed to be the same in other models. The sensitivity of the model outcomes to ligament material properties confirms that calibration of these parameters is critical and using literature values may not be appropriate.

Keywords: contact mechanics; finite element modeling; knee modeling; sensitivity analysis; tibiofemoral joint.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Anterior (full-length bones) and posterior (articulating region) view of the four models.
FIGURE 2
FIGURE 2
Examples of ligament attachment node sets 1 & 2 involved in the ligament–bone tied contacts. Red nodes indicate selected nodes in the set.
FIGURE 3
FIGURE 3
Analysis workflows of the four outcome parameters.
FIGURE 4
FIGURE 4
Convergence (left) and valgus kinematics (right) results for all valgus simulations of the Ligament prestretch factor MCL parameter.
FIGURE 5
FIGURE 5
Peak contact pressure location (left) and peak contact pressure (right) results for all valgus simulations of the Ligament prestretch factor MCL parameter.
FIGURE 6
FIGURE 6
Ligament prestretch factor sensitivity analysis simulation results rated on influence on convergence, valgus kinematics, peak contact pressure, and location of peak contact pressure (0 (No influence) to 3 (Large influence)).
FIGURE 7
FIGURE 7
Ligament Young’s modulus sensitivity analysis simulation results rated on influence on convergence, valgus kinematics, peak contact pressure, and location of peak contact pressure (0 (No influence) to 3 (Large influence)).
FIGURE 8
FIGURE 8
Ligament attachment condition sensitivity analysis simulation results rated on influence on convergence, valgus kinematics, peak contact pressure, and location of peak contact pressure (0 (No influence) to 3 (Large influence)).
FIGURE 9
FIGURE 9
The influence of the Augmented Lagrangian (A) and Penalty factor (B) on the peak contact pressure in valgus rotation.
FIGURE 10
FIGURE 10
Tibiofemoral cartilage contact parameters sensitivity analysis simulation results rated on influence on convergence, valgus kinematics, peak contact pressure, and location of peak contact pressure (0 (No influence) to 3 (Large influence)).
See this image and copyright information in PMC

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