Polynomial chaos expansion based sensitivity analysis of predicted knee reactions—assessing the influence of the primary ligaments in distraction based models

Abstract

Computational knee models have shown that predicted condylar reactions are sensitive to the utilized ligament mechanical parameters. These models, however, are computationally expensive with multiple sources of uncertainty. Traditional uncertainty analysis using Monte-Carlo (MC) inspired methods are costly to perform. The purpose of this study was to use two example calibrated knee models to compare quasi-MC versus polynomial chaos expansion (PCE) sensitivity analyses of predicted condylar reactions that included uncertainty in the mechanical parameters of the ligaments. PCE was practically identical versus quasi-MC with 95% and 98% reductions in model evaluations for analyses with 10 and 6 uncertain variables, respectively.

Keywords:

• Knee model
• polynomial chaos
• Sobol’s indices
• sensitivity analysis
• Monte Carlo

Disclosure statement

Authors have no conflict of interest.

Additional information

Funding

Stryker for funding initial development of the calibrated models.