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

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There is high demand for knee replacements, and greater efforts are being invested into the pre-clinical analysis of TKR designs to improve their performance in-vivo. A wide range of experimental and computational methods are used to analyse TKR performance. However, direct validation of these methods is limited by the restrictions and challenges of clinical assessment, and confounded by the high variability of results seen in-vivo.

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.

Computational model
Computational model

Related research groups

Bioengineering Science
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