心血管数字孪生：从基于物理的方法到数据驱动的方法

中文摘要 AI 辅助

心血管数字孪生旨在创建随临床数据演化的患者特异性计算模型，以支持诊断、预后和治疗优化。机理模型具有生理可解释性但计算量大，而数据驱动方法提升了可扩展性但存在鲁棒性有限的风险。新兴的基于物理信息、基于图的及混合方法将物理约束与血管网络的关系学习相结合，我们综述了建模范式、数据同化框架、验证挑战及迈向临床可部署心血管数字孪生的转化路径。

英文摘要

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.

发表机构

• Sheffield Hallam University（谢菲尔德哈勒姆大学）
• Institute of Development, Aging and Cancer (IDAC), Tohoku University（东北大学开发、衰老与癌症研究所（IDAC））

机构由 AI 辅助整理，请以论文原文为准。