title: A multi-platform comparison of efficient probabilistic methods in the prediction of total knee replacement mechanics.
abstract: Explicit finite element (FE) and multi-body dynamics (MBD) models have been developed to evaluate total knee replacement (TKR) mechanics as a complement to experimental methods. In conjunction with these models, probabilistic methods have been implemented to predict performance bounds and identify important parameters, subject to uncertainty in component alignment and experimental conditions. Probabilistic methods, such as advanced mean value (AMV) and response surface method (RSM), provide an efficient alternative to the gold standard Monte Carlo simulation technique(MCST). The objective of the current study was to benchmark models from three platforms (two FE and one MBD) using various probabilistic methods by predicting the influence of alignment variability and experimental parameters on TKR mechanics in simulated gait. Predicted kinematics envelopes were on average about 2.6mm for tibial anterior-posterior translation, 2.98 for tibial internal–external rotation and 1.9 MPa for tibial peak contact pressure for the various platforms and methods. Based on this good agreement with the MCST, the efficient probabilistic techniques may prove useful in the fast evaluation of new implant designs, including considerations of uncertainty, e.g. misalignment.
authors.affiliation: Bioengineering Science Research Group, School of Engineering Sciences, University of Southampton, Southampton, UK. mike.strickland@soton.ac.uk
authors.forename: M A
authors.initials: MA
authors.lastname: Strickland
authors.forename: C T C
authors.initials: CT
authors.lastname: Arsene
authors.forename: S
authors.initials: S
authors.lastname: Pal
authors.forename: P J
authors.initials: PJ
authors.lastname: Laz
authors.forename: M
authors.initials: M
authors.lastname: Taylor
mesh_terms: D019645:Arthroplasty, Replacement, Knee; D001696:Biomechanical Phenomena; D020342:Finite Element Analysis; D006801:Humans; D009010:Monte Carlo Method; D011336:Probability; D013977:Tibia / Q000502:physiology; D035501:Uncertainty