Cardiovascular Digital Twins from Physics Based to Data Driven Approaches
Emmanuel Lwele, Francis Chikweto
physics.med-ph cs.LG
Abstract
Cardiovascular digital twins aim to create patient-specific computational models that evolve with clinical data to support diagnosis, prognosis, and therapy optimisation. Mechanistic models provide physiological interpretability but remain computationally demanding, whereas data-driven approaches improve scalability yet risk limited robustness. Emerging physics-informed, graph-based, and hybrid methods integrate physical constraints with relational learning across vascular networks. We review modelling paradigms, data assimilation frameworks, validation challenges, and translational pathways toward clinically deployable cardiovascular digital twins.
Topics
Classified with taxonomy v2 on Wed, 2 Sept 2026.