Michael A Strickland

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The capability to predict in-vivo wear of knee replacements is a valuable pre-clinical analysis tool for implant designers. Traditionally, time-consuming experimental tests provided the principal means of investigating wear. Today, computational models offer an alternative. However, the validity of these models has not been demonstrated across a range of(More)
4 rate following a change in direction. Abstract (200 words) Computational tools are emerging as design tools for the development of total joint replacement with 1 improved wear performance. The current wear models of polyethylene assume that wear is linearly 2 proportional to sliding distance; however, it is hypothesised that the wear rate varies and is(More)
Improving total knee replacement (TKR) requires better understanding of the many factors influencing clinical outcomes. Recently, probabilistic studies have investigated the influence of variability for individual TKR activities. This study demonstrates conceptually how probabilistic studies might further provide a framework to explore relationships not(More)
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(More)
Patient-specific finite element models of the implanted proximal femur can be built from pre-operative computed tomography scans and post-operative X-rays. However, estimating three-dimensional positioning from two-dimensional radiographs introduces uncertainty in the implant position. Further, accurately measuring the thin cement mantle and the degree of(More)
Experimental testing is widely used to predict wear of total knee replacement (TKR) devices. Computational models cannot replace this essential in-vitro testing, but they do have complementary strengths and capabilities, which make in-silico models a valuable support tool for experimental wear investigations. For effective exploitation, these two separate(More)
1. ABSTRACT Probabilistic Finite Element (FE) models have recently been developed to assess the impact of experimental variability present in knee wear simulator on predicted Total Knee Replacement (TKR) mechanics by determining the performance envelope of joint kinematics and contact mechanics. The gold standard for this type of analysis is currently the(More)
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