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A multi-platform comparison of efficient probabilistic methods in the prediction of total knee replacement mechanics

Strickland, M.A., Arsene, C.T.C., Pal, S., Laz, P.J. and Taylor, M. (2010) A multi-platform comparison of efficient probabilistic methods in the prediction of total knee replacement mechanics Computer Methods in Biomechanics and Biomedical Engineering, 13, (6), pp. 701-709. (doi:10.1080/10255840903476463). (PMID:20162473).

Record type: Article

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 2.6 mm for tibial anterior-posterior translation, 2.9° 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.

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e-pub ahead of print date: 9 December 2010
Published date: 2010
Keywords: total knee replacement, kinematics, contact mechanics, knee mechanics, probabilistic methods, simulation
Organisations: Bioengineering Group

Identifiers

Local EPrints ID: 143555
URI: http://eprints.soton.ac.uk/id/eprint/143555
ISSN: 1025-5842
PURE UUID: e1fb9b91-04da-4310-9dad-c8c0ccffb88c

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Date deposited: 12 Apr 2010 12:11
Last modified: 18 Jul 2017 23:09

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Contributors

Author: M.A. Strickland
Author: C.T.C. Arsene
Author: S. Pal
Author: P.J. Laz
Author: M. Taylor

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