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This article reviews the evolution of cardiovascular digital twins, focusing on hybrid methodologies that combine mechanistic physics with data-driven machine learning to balance interpretability and scalability in clinical applications. By integrating graph-based relational learning across vascular networks, these models aim to overcome computational limits while maintaining physiological accuracy for diagnosis and therapy optimization. The work represents a significant advancement at the intersection of medical modeling and advanced AI techniques like physics-informed neural networks.
Cardiovascular Digital Twins from Physics Based to Data Driven Approaches
arXiv:2608.02135v1 Announce Type: cross 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.
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.
Medical Physics (physics.med-ph); Machine Learning (cs.LG)
arXiv:2608.02135 [physics.med-ph]
(or arXiv:2608.02135v1 [physics.med-ph] for this version)
https://doi.org/10.48550/arXiv.2608.02135 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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