Cardiovascular Digital Twins from Physics Based to Data Driven Approaches

Cardiovascular diseases remain a leading cause of global mortality, yet clinical practice often relies on population-based risk models that fail to account for individual physiological variability. Cardiovascular digital twins aim to bridge this gap by creating dynamic, patient-specific computational representations. Unlike static simulation models, a true digital twin requires bidirectional coupling: it must assimilate real-time clinical data to update its internal state and provide actionable feedback to clinicians for diagnosis, prognosis, and therapy optimization.

The field is currently navigating a spectrum of modelling approaches. Traditional biophysical models—such as 0D lumped-parameter and 1D haemodynamic formulations—offer high physiological interpretability and computational efficiency, making them ideal for real-time monitoring. Conversely, 3D computational fluid dynamics (CFD) and electromechanical solvers provide high spatial resolution but are often too computationally demanding for rapid clinical deployment.

Emerging data-driven methods, including machine learning and deep learning, offer superior scalability and the ability to handle complex, high-dimensional datasets. However, these models often lack the physical consistency required for medical safety. The current research frontier focuses on hybrid methods, such as Physics-Informed Neural Networks (PINNs) and Graph Neural Networks (GNNs), which attempt to embed physical laws directly into the learning process to ensure that predictions remain physiologically plausible while benefiting from the speed of data-driven inference.

For digital twins to move from research to the bedside, several hurdles must be cleared. Data assimilation remains a primary challenge, as clinical data are often heterogeneous, noisy, and incomplete. Furthermore, there is a critical need for robust uncertainty quantification; clinicians must understand the confidence levels of a model's predictions before using them to guide invasive interventions. Finally, the field requires standardized validation protocols to ensure that these virtual replicas are as reliable as the physical measurements they are intended to augment.