Digital twins and digital models of the human circulatory system

Abstract

Digital models and digital twins of human circulatory transport could transform the way cardiovascular and haematological diseases are understood, monitored and treated. Digital twins are dynamic virtual representations of physical systems that continuously assimilate real-world data to simulate and predict system behaviour. However, translating digital twins into clinical practice remains challenging owing to the complexity of human physiology and the need for continuous bidirectional coupling between virtual models and their physical counterparts. Advances in medical-grade sensors, wearable devices, microfluidics, artificial intelligence and high-performance computing are accelerating the evolution of digital models into clinically meaningful digital twins. In this Review, we examine how digital twins can model the human circulatory system across scales, from macroscopic blood flow to molecular and cellular transport. We outline the essential components of a circulatory-transport digital twin, describe the pathophysiological conditions that can be digitally represented, and discuss approaches for acquiring and integrating physiological data, computational modelling strategies and model-based inference. We further survey applications of digital models and digital twins across various types of model inferences, from mechanistic insights to clinical decisions such as disease diagnosis, risk stratification, surgical planning and treatment planning. Finally, we identify key challenges and opportunities for next-generation circulatory digital twins capable of real-time monitoring, predictive simulation and closed-loop therapeutic control. Digital twins of the human circulatory system aim to integrate real-time physiological data with computational models to understand, monitor and treat cardiovascular and haematological diseases. This Review outlines the components, clinical applications and emerging technologies enabling such digital twins, highlighting the remaining challenges to achieving real-time, predictive and closed-loop clinical use.