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

  • Computational Biomechanics for Patient-Specific Applications
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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.

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Acknowledgments

The authors would like to acknowledge funding from the Engineering and Physical Sciences Research Council (EP/G0075727/2), the Wellcome Trust Medical Engineering Centre at King’s College London (WT 088641/Z/09/Z). PL holds a Sir Henry Dale Fellowship funded jointly by the Wellcome Trust and the Royal Society (Grant No. 099973/Z/12/Z). This research was supported by the National Institute for Health Research (NIHR) Biomedical Research Centre at Guy’s and St. Thomas’ NHS Foundation Trust and King’s College London. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.

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Associate Editor Karol Miller oversaw the review of this article.

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Lamata, P., Cookson, A. & Smith, N. Clinical Diagnostic Biomarkers from the Personalization of Computational Models of Cardiac Physiology. Ann Biomed Eng 44, 46–57 (2016). https://doi.org/10.1007/s10439-015-1439-8

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