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Related Experiment Video

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Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
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Computational fluid dynamics modelling in cardiovascular medicine.

Paul D Morris1, Andrew Narracott2, Hendrik von Tengg-Kobligk3

  • 1Department of Cardiovascular Science, University of Sheffield, Sheffield, UK Insigneo Institute for In Silico Medicine, Sheffield, UK Department of Cardiology, Sheffield Teaching Hospitals NHS Trust, Sheffield, UK.

Heart (British Cardiac Society)
|October 30, 2015
PubMed
Summary
This summary is machine-generated.

Computational fluid dynamics (CFD) modelling is revolutionizing cardiovascular medicine by enabling personalized risk prediction and virtual treatment planning. While offering significant benefits, challenges in its clinical translation are being actively addressed.

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Area of Science:

  • Cardiovascular Medicine
  • Biomedical Engineering
  • Computational Science

Background:

  • Computational fluid dynamics (CFD) is a well-established engineering tool increasingly applied to cardiovascular systems.
  • CFD modelling offers advantages in prototyping medical devices like stents and valves.
  • Integration with cardiovascular imaging allows for detailed physiological analysis and computation of unmeasurable metrics.

Purpose of the Study:

  • To review the methods, benefits, and challenges of adopting and translating CFD modelling in cardiovascular medicine.
  • To highlight the potential of CFD in patient-specific and multi-scale modeling for personalized medicine.
  • To discuss the transition of CFD models into clinical tools for various cardiovascular diseases.

Main Methods:

  • Review of existing literature on CFD applications in cardiovascular research and clinical practice.
  • Analysis of the benefits, including rapid prototyping and detailed physiological characterization.
  • Examination of challenges related to methodological, regulatory, and educational aspects.

Main Results:

  • CFD modelling has revolutionized cardiovascular device development and enables detailed physiological assessment.
  • Patient-specific and multi-scale CFD models facilitate individualized risk prediction and virtual treatment planning.
  • CFD integration is moving towards 'digital patient' representations, potentially reducing clinical trial costs and risks.

Conclusions:

  • CFD modelling represents a paradigm shift in cardiovascular medicine, offering significant potential for personalized patient care.
  • Addressing methodological, regulatory, and educational challenges is crucial for widespread clinical adoption.
  • The continued development and integration of CFD promise a new era in cardiovascular diagnostics and therapeutics.