Research project: Enhanced pre-clinical assesment of total knee replacement using computational modelling with experimental corroboration and probabili

Consequently, the need exists to achieve greater corroboration between in-silico and in-vitro testing, and identify the key sources of uncertainty, so that greater confidence can be placed in these assessment tools. A new generation of fast computational TKR models were developed, with closer collaboration with experimental researchers than previously. The models were extended to include in-silico wear prediction, patellofemoral & full lower limb models, control systems, and accurate system dynamics. These models were employed within a probabilistic framework to provide a statistically-quantified assessment of the input factors most influential to variability in the mechanical outcomes of TKR testing. This gives designers a much richer holistic visibility of the true system behaviour than existing ‘deterministic’ simulation approaches.