Cardiovascular Digital Twins from Physics Based to Data Driven Approaches
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.
| Authors | Emmanuel Lwele, Francis Chikweto |
|---|---|
| Primary category | physics.med-ph |
| Published | 3 August 2026 |
| Last updated at source | 3 August 2026 |
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