Cardiac digital twins are personalized virtual representations used to understand complex heart mechanisms. Solving the ECG inverse problem is crucial for accurate virtual heart modelling, enabling the derivation of internal electrical activity information from recorded surface potentials. Despite challenges from cardiac complexity, noisy ECG data, and computational efficiency, recent advancements hold significant promise for enhancing virtual heart modelling, ultimately advancing precision medicine in cardiology. This paper aims to provide a comprehensive review of the methods of solving ECG inverse problem, the validation strategies, the clinical applications, and future perspectives. For the computing methodologies, we broadly classify state-of-the-art approaches into two categories: deterministic and probabilistic methods, including 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 cardiac digital twins.

翻译：心脏数字孪生是一种用于理解复杂心脏机制的个性化虚拟表征。求解心电图逆问题对于构建精确的虚拟心脏模型至关重要，它使得从记录的体表电位推导内部电活动信息成为可能。尽管面临心脏结构复杂性、心电图数据噪声以及计算效率等多重挑战，该领域的最新进展为提升虚拟心脏建模水平带来了重要希望，最终将推动心脏病学精准医疗的发展。本文旨在对心电图逆问题的求解方法、验证策略、临床应用及未来前景进行全面综述。在计算方法论层面，我们将现有先进方法大致分为两类：确定性方法与概率性方法，其中涵盖了传统技术与基于深度学习的技术。将物理定律与深度学习模型相融合展现出广阔前景，但如何精确捕捉动态电生理特性、获取准确领域知识以及量化预测不确定性等挑战依然存在。将模型整合至临床工作流程，并确保其对医疗专业人员具有可解释性与易用性至关重要。克服这些挑战将推动心脏数字孪生领域的进一步研究。