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Elsevier, Journal of Biomechanics, 9(44), p. 1666-1672

DOI: 10.1016/j.jbiomech.2011.03.024

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Patient-specific finite element analysis of the human femur—A double-blinded biomechanical validation

Journal article published in 2011 by Nir Trabelsi, Zohar Yosibash, Christof Wutte, Peter Augat ORCID, Sebastian Eberle
This paper is available in a repository.
This paper is available in a repository.

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Data provided by SHERPA/RoMEO

Abstract

Patient-specific finite element (PSFE) models based on quantitative computer tomography (qCT) are generally used to "predict" the biomechanical response of human bones with the future goal to be applied in clinical decision-making. However, clinical applications require a well validated tool that is free of numerical errors and furthermore match closely experimental findings. In previous studies, not all measurable data (strains and displacements) were considered for validation. Furthermore, the same research group performed both the experiments and PSFE analyses; thus, the validation may have been biased. The aim of the present study was therefore to validate PSFE models with biomechanical experiments, and to address the above-mentioned issues of measurable data and validation bias. A PSFE model (p-method) of each cadaver femur (n = 12) was generated based on qCT scans of the specimens. The models were validated by biomechanical in-vitro experiments, which determined strains and local displacements on the bone surface and the axial stiffness of the specimens. The validation was performed in a double-blinded manner by two different research institutes to avoid any bias. Inspecting all measurements (155 values), the numerical results correlated well with the experimental results (R(2) = 0.93, slope 1.0093, mean of absolute deviations 22%). In conclusion, a method to generate PSFE models from qCT scans was used in this study on a sample size not yet considered in the past, and compared to experiments in a douple-blinded manner. The results demonstrate that the presented method is in an advanced stage, and can be used in clinical computer-aided decision-making.