Orthopaedic implants are designed and configured with the intent to restore functionality to a degenerated joint thus restoring relatively pain free mobility to afflicted patients. Stress analyses play a critical role in understanding and improving joint replacement performance and prosthesis system technology and finite element analysis (FEA) is the method of choice to conduct these analyses. These methods are ideally suited to simulate the behavior of complex biological structures that often include complicated geometry, non-linear material behavior, and time dependent phenomenon. A major limitation of the application of computational methods in orthopaedic research, however, is their inability to account for uncertainty in important system parameters such as joint loading, biological material properties, and anatomy. This uncertainty has a direct effect on the ability to predict the structural response of the system and therefore its reliability.
The impact of uncertainty on the structural performance and potential lifetime of implant systems is largely unexplored. Hence, advanced probabilistic analysis methods are used to investigate how uncertainties influence the predicted performance of a cemented hip implant system. Uncertainty is accounted for by modeling system input parameters, such as joint loading and material properties, as random variables. The probabilistic response of the cemented prosthesis is quantified by computing cumulative distribution functions of several structural responses typically used to assess the performance of these systems. Uncertainty in input random variables resulted in significant uncertainty in the computed performance measures, which had computed standard deviations of up to 63% of the mean. Prosthesis reliability is investigated using performance functions that describe specific modes of prosthesis failure. Prosthesis-bone cement interface debonding was the most likely m ode of failure, with predicted failure probabilities of up to 98%. The ability of deterministic shape optimization to reduce the probability of failure of the prosthesis in explored. Deterministic design optimization significantly reduced the predicted probability of failure using all performance functions. A probabilistic design optimization method is developed and used to investigate prosthesis shapes that minimize the probability of prosthesis failure. Designs that resulted in minimum deterministic stress values did not necessarily result in minimum probability of failure. For all probabilistic analyses performed, the most important random variables, ranked using probabilistic sensitivity factors, were the joint loads and the material strengths.
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