从物理模型到数据驱动，构建可随临床数据演化的个性化心血管数字孪生
Cardiovascular Digital Twins from Physics Based to Data Driven Approaches
- 融合物理规律与图神经网络，实现血管网络的动态建模
- 兼顾生理可解释性与计算效率，提升模型鲁棒性
- 适合临床决策支持与个性化治疗优化研究者参考
心血管数字孪生旨在构建随临床数据动态演化的患者特异性计算模型，用于辅助诊断、预后评估和治疗优化。机制模型具有生理可解释性，但计算成本高；数据驱动方法虽提升可扩展性，却可能降低鲁棒性。新兴的物理信息引导、基于图结构及混合方法将物理约束与血管网络中的关系学习相结合。本文综述了建模范式、数据同化框架、验证挑战及向临床可用数字孪生转化的路径。
原文摘要 · Abstract (English)
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.
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