Clinical Diagnostic Biomarkers from the Personalization of Computational Models of Cardiac Physiology

Pablo Lamata, Andrew Cookson, Nic Smith

Research output: Contribution to journalArticlepeer-review

7 Citations (SciVal)

Abstract

Computational modelling of the heart is rapidly advancing to the point of clinical utility. However, the difficulty of parameterizing and validating models from clinical data indicates that the routine application of truly predictive models remains a significant challenge. We argue there is significant value in an intermediate step towards prediction. This step is the use of biophysically based models to extract clinically useful information from existing patient data. Specifically in this paper we review methodologies for applying modelling frameworks for this goal in the areas of quantifying cardiac anatomy, estimating myocardial stiffness and optimizing measurements of coronary perfusion. Using these indicative examples of the general overarching approach, we finally discuss the value, ongoing challenges and future potential for applying biophysically based modelling in the clinical context.
Original languageEnglish
Pages (from-to)46-57
JournalAnnals of Biomedical Engineering
Volume44
Issue number1
DOIs
Publication statusPublished - 31 Jan 2016

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