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Prediction of the intramembranous tissue formation during perisprosthetic healing with uncertainties. Part 2. Global clinical healing due to combination of random sources

Author

Listed:
  • J. Yang
  • B. Faverjon
  • D. Dureisseix
  • P. Swider
  • S. Marburg
  • H. Peters
  • N. Kessissoglou

Abstract

This work proposes to examine the variability of the bone tissue healing process in the early period after the implantation surgery. The first part took into account the effect of variability of individual biochemical factors on the solid phase fraction, which is an indicator of the quality of the primary fixation and condition of its long-term behaviour. The next issue, addressed in this second part, is the effect of cumulative sources of uncertainties on the same problem of a canine implant. This paper is concerned with the ability to increase the number of random parameters to assess the coupled influence of those variabilities on the tissue healing. To avoid an excessive increase in the complexity of the numerical modelling and further, to maintain efficiency in computational cost, a collocation-based polynomial chaos expansion approach is implemented. A progressive set of simulations with an increasing number of sources of uncertainty is performed. This information is helpful for future implant design and decision process for the implantation surgical act.

Suggested Citation

  • J. Yang & B. Faverjon & D. Dureisseix & P. Swider & S. Marburg & H. Peters & N. Kessissoglou, 2016. "Prediction of the intramembranous tissue formation during perisprosthetic healing with uncertainties. Part 2. Global clinical healing due to combination of random sources," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 19(13), pages 1387-1394, October.
    • Handle: RePEc:taf:gcmbxx:v:19:y:2016:i:13:p:1387-1394
    DOI: 10.1080/10255842.2016.1143465
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    References listed on IDEAS

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    1. S. S. Isukapalli & A. Roy & P. G. Georgopoulos, 1998. "Stochastic Response Surface Methods (SRSMs) for Uncertainty Propagation: Application to Environmental and Biological Systems," Risk Analysis, John Wiley & Sons, vol. 18(3), pages 351-363, June.
    2. Pascal Swider & D. Ambard & G. Guérin & Kjeld Søballe & Joan Bechtold, 2011. "Sensitivity analysis of periprosthetic healing to cell migration, growth factor and post-operative gap using a mechanobiological model," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 14(09), pages 763-771.
    3. R. Vayron & E. Barthel & V. Mathieu & E. Soffer & F. Anagnostou & G. Haiat, 2011. "Variation of biomechanical properties of newly formed bone tissue determined by nanoindentation as a function of healing time," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 14(S1), pages 139-140.
    4. Oladyshkin, S. & Nowak, W., 2012. "Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 179-190.
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