111. Cardiovascular modelling and clinical applications

Abstract

Cardiovascular diseases remain the leading cause of death globally.

The use of computer models to simulate the functioning of the human body is viewed increasingly as one of the most promising tools to further develop our understanding of human pathophysiology, and therefore improve prevention, diagnosis, and treatment of human disease.

A significant motivating factor for the deployment of biomedical codes in the clinical management of cardiovascular disease is the development of human digital twins.

Such models would allow personalised guidance for healthcare, disease diagnosis and treatment, and wellbeing for specific individuals.

The seminar will focus on the research carried out within my group on the characterization of blood flows using patient-specific Navier-Stokes modelling approaches, machine learning (Gaussian Process Emulators) as well as experimental approaches (e.g., particle image velocimetry, ultrasound) to ultimately improve diagnosis and management of cardiovascular diseases such as intracranial aneurysms, ischaemic stroke and chronic kidney disease.

About the author

Dr Marzo is an Associate Professor (Senior Lecturer) of Cardiovascular Biomechanics at the Department of Mechanical Engineering, University of Sheffield, UK.

In the early stages of his PhD at the University of Sheffield, he was awarded the David Crighton Fellowship, and spent one year of his doctoral studies at the University of Cambridge.

Here he investigated the mechanistic theory behind flow-induced oscillations in elastic vessels.

In 2011-12 he worked as a Principal Research Scientist for the UK National Health Service to support technology development, dissemination, and adoption, for people with long-term disabilities.

Dr Marzo’s research area stands at the interface between the physical and life sciences.

It is firmly rooted in engineering and mathematics in the context of computational fluid mechanics and cardiovascular biomechanics, but has a strong emphasis on clinical interpretation and translation of engineering technologies into clinical tools.