Cardiovascular models for personalised medicine: Where now and where next?

Abstract

The aim of this position paper is to provide a brief overview of the current status of cardiovascular modelling and of the processes required and some of the challenges to be addressed to see wider exploitation in both personal health management and clinical practice. In most branches of engineering the concept of the Digital Twin, informed by extensive and continuous monitoring and coupled with robust data assimilation and simulation techniques, is gaining traction: the Gartner Group listed it as one of the top ten digital trends in 2018. The cardiovascular modelling community is starting to develop a much more systematic approach to the combination of physics, mathematics, control theory, artificial intelligence, machine learning, computer science and advanced engineering methodology, as well as working more closely with the clinical community to better understand and exploit physiological measurements, and indeed to develop jointly better measurement protocols informed by model-based understanding. Aspects of developments in physiological modelling, model personalisation, model outcome uncertainty, and the clinical decision support based on this, will be

Markov chain Monte Carlowith Gaussian processes for fast parameter estimation and uncertainty quantification in a1Dfluid‐dynamics model of the pulmonary circulation

“…Before being used as a decision support system in the clinic, these models must first be adapted to patient‐specific conditions and an assessment of their credibility (uncertainty quantification, UQ) based on a comparison between model predictions and clinical data, must be performed. For example, studies in References 1 and 2 discuss the requirements that clinically applicable cardiovascular models must meet and the advances required to do so. Parameter inference and uncertainty quantification in biophysical models of physiological processes, and cardiovascular processes in particular is a challenging task to accomplish, as the physical models, typically expressed in terms of coupled non‐linear PDEs, with no closed‐form solution, are becoming more complex.…”

Data-driven cardiovascular flow modelling: examples and opportunities

“…Combining data-driven modelling and machine learning with physics-based simulations provides a hybrid modelling strategy [132] with high potential in multiscale modelling of biological systems [129]. Advancements in each of these fields and their coupling will produce predictive digital twins [133] that could facilitate treatment planning, patient management and ultimately transform personalized cardiovascular medicine [134].…”

Model Verification and Error Sensitivity of Turbulence-Related Tensor Characteristics in Pulsatile Blood Flow Simulations

“…These so-called turbulent-like conditions often prevails at valvular/vascular malformations, but have lately also been found or presupposed under apparent normal physiological flows [4,5]. To-date, the most common modalities to non-invasively estimated these flow conditions are via high-fidelity magnetic resonance imaging (MRI) techniques [6,7] or computational fluid dynamics (CFD) simulation methods [8,9]. Appropriate verification, validation and uncertainty quantification in (image-based) patient-specific cardiovascular numerical modeling are essential steps in order to approach clinical utility [9][10][11][12].…”

“…To-date, the most common modalities to non-invasively estimated these flow conditions are via high-fidelity magnetic resonance imaging (MRI) techniques [6,7] or computational fluid dynamics (CFD) simulation methods [8,9]. Appropriate verification, validation and uncertainty quantification in (image-based) patient-specific cardiovascular numerical modeling are essential steps in order to approach clinical utility [9][10][11][12]. State-of-the-art modeling praxis has been well covered for laminar vascular flows [8,12,13], while general guidelines related to numerical predictions of turbulent-like hemodynamics have received less attention, in spite of the growing number of published turbulence-related CFD studies within the research community.…”