Uncertainty quantification for personalized analyses of human proximal femurs

Wille, H., Ruess, M. , Rank, E. and Yosibash, Z. (2016) Uncertainty quantification for personalized analyses of human proximal femurs. Journal of Biomechanics, 49(4), pp. 520-527. (doi: 10.1016/j.jbiomech.2015.11.013) (PMID:26873282)

Abstract

Computational models for the personalized analysis of human femurs contain uncertainties in bone material properties and loads, which affect the simulation results. To quantify the influence we developed a probabilistic framework based on polynomial chaos (PC) that propagates stochastic input variables through any computational model. We considered a stochastic E-ρ relationship and a stochastic hip contact force, representing realistic variability of experimental data. Their influence on the prediction of principal strains (ϵ1 and ϵ3) was quantified for one human proximal femur, including sensitivity and reliability analysis. Large variabilities in the principal strain predictions were found in the cortical shell of the femoral neck, with coefficients of variation of Math Eq. Between 60-80% of the variance in ϵ1 and ϵ3 are attributable to the uncertainty in the E-ρ relationship, while Math Eq are caused by the load magnitude and 5-30% by the load direction. Principal strain directions were unaffected by material and loading uncertainties. The antero-superior and medial inferior sides of the neck exhibited the largest probabilities for tensile and compression failure, however all were very small (Math Eq). In summary, uncertainty quantification with PC has been demonstrated to efficiently and accurately describe the influence of very different stochastic inputs, which increases the credibility and explanatory power of personalized analyses of human proximal femurs.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Ruess, Dr Martin
• Authors: Wille, H., Ruess, M., Rank, E., and Yosibash, Z.
• College/School: College of Science and Engineering > School of Engineering > Infrastructure and Environment
• Journal Name: Journal of Biomechanics
• Publisher: Elsevier
• ISSN: 0021-9290
• ISSN (Online): 1873-2380
• Published Online: 02 December 2015
• Copyright Holders: Copyright © 2016 Elsevier
• First Published: First published in Journal of Biomechanics 49(4):520-527
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher.