🤖 AI Summary
This study addresses the challenge of balancing interpretability and scalability in the clinical deployment of cardiovascular digital twins. To this end, it proposes a hybrid modeling paradigm that integrates physics-based mechanistic models, data-driven approaches, and physics-informed graph neural networks, all unified through data assimilation to enable dynamic, patient-specific vascular network modeling. The work systematically traces the evolution of modeling paradigms in this domain, identifies key barriers to clinical translation, and establishes a technical roadmap alongside a validation framework for developing digital twins that simultaneously achieve physiological interpretability, computational efficiency, and clinical applicability.
📝 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.