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1.
Comput Methods Biomech Biomed Engin ; 19(13): 1378-86, 2016 Oct.
Article in English | MEDLINE | ID: mdl-26881777

ABSTRACT

A stochastic model is proposed to predict the intramembranous process in periprosthetic healing in the early post-operative period. The methodology was validated by a canine experimental model. In this first part, the effects of each individual uncertain biochemical factor on the bone-implant healing are examined, including the coefficient of osteoid synthesis, the coefficients of haptotactic and chemotactic migration of osteoblastic population and the radius of the drill hole. A multi-phase reactive model solved by an explicit finite difference scheme is combined with the polynomial chaos expansion to solve the stochastic system. In the second part, combined biochemical factors are considered to study a real configuration of clinical acts.


Subject(s)
Prostheses and Implants , Uncertainty , Wound Healing , Animals , Chemotaxis , Dogs , Humans , Membranes , Models, Biological , Numerical Analysis, Computer-Assisted , Stochastic Processes
2.
Comput Methods Biomech Biomed Engin ; 19(13): 1387-94, 2016 Oct.
Article in English | MEDLINE | ID: mdl-26867011

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.


Subject(s)
Prostheses and Implants , Uncertainty , Wound Healing , Algorithms , Animals , Dogs , Membranes , Numerical Analysis, Computer-Assisted
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