In-silico predictions of TKR robustness to wear variability: a probabilistic cross-design comparison

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Description/Abstract

Wear of TKR is a key concern for designers, but is highly variable in clinical retrievals. Conventional in-vitro knee wear simulators are limited to relatively small numbers of tests and cannot fully address this uncertainty; in-silico models can use large numbers of trials with low associated time & cost. Using probabilistic methods we can explore whether input variability (e.g. component mal-positioning) can account for the high degree of wear variability observed.

Because we are able to simulate many trials, we can also explore the predictions of different wear algorithms, and also run studies concurrently for different TKR designs, allowing us to compare implant designs and observe whether some are more robust to wear variability than others.