Li Lei, Camps Julia, Rodriguez Blanca, Grau Vicente
IEEE Rev Biomed Eng. 2025;18:316-336. doi: 10.1109/RBME.2024.3486439. Epub 2025 Jan 28.
Cardiac digital twins (CDTs) are personalized virtual representations used to understand complex cardiac mechanisms. A critical component of CDT development is solving the ECG inverse problem, which enables the reconstruction of cardiac sources and the estimation of patient-specific electrophysiology (EP) parameters from surface ECG data. Despite challenges from complex cardiac anatomy, noisy ECG data, and the ill-posed nature of the inverse problem, recent advances in computational methods have greatly improved the accuracy and efficiency of ECG inverse inference, strengthening the fidelity of CDTs. This paper aims to provide a comprehensive review of the methods for solving ECG inverse problems, their validation strategies, their clinical applications, and their future perspectives. For the methodologies, we broadly classify state-of-the-art approaches into two categories: deterministic and probabilistic methods, including both conventional and deep learning-based techniques. Integrating physics laws with deep learning models holds promise, but challenges such as capturing dynamic electrophysiology accurately, accessing accurate domain knowledge, and quantifying prediction uncertainty persist. Integrating models into clinical workflows while ensuring interpretability and usability for healthcare professionals is essential. Overcoming these challenges will drive further research in CDTs.
心脏数字孪生体（CDTs）是用于理解复杂心脏机制的个性化虚拟模型。CDT开发的一个关键组成部分是解决心电图逆问题，该问题能够从体表心电图数据重建心脏源并估计患者特异性电生理（EP）参数。尽管存在来自复杂心脏解剖结构、嘈杂的心电图数据以及逆问题不适定性等挑战，但计算方法的最新进展极大地提高了心电图逆推断的准确性和效率，增强了CDTs的逼真度。本文旨在对解决心电图逆问题的方法、其验证策略、临床应用及其未来前景进行全面综述。对于方法，我们将最先进的方法大致分为两类：确定性方法和概率性方法，包括传统技术和基于深度学习的技术。将物理定律与深度学习模型相结合具有前景，但仍存在诸如准确捕捉动态电生理、获取准确领域知识以及量化预测不确定性等挑战。在确保医疗保健专业人员的可解释性和可用性的同时，将模型集成到临床工作流程中至关重要。克服这些挑战将推动CDTs的进一步研究。
