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.

心脏数字孪生是个体化的虚拟表示，用于理解复杂的心脏机制。本文综述了解决心电图反问题的方法、验证策略、临床应用和未来展望，涵盖了确定性和概率方法，包括传统和基于深度学习的技术。整合物理定律与深度学习模型有潜力，但需要克服动态电生理学的准确捕捉、获得准确的领域知识和量化预测不确定性等挑战。将模型整合到临床工作流中，确保对医疗专业人员的解释性和可用性至关重要，克服这些挑战将推动心脏数字孪生领域的进一步研究。