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。

心脏数字双生体的逆问题求解：综述

Solving the Inverse Problem of Electrocardiography for Cardiac Digital  Twins: A Survey

The identification of the Purkinje conduction system in the heart is a
challenging task, yet essential for a correct definition of cardiac digital
twins for precision cardiology. Here, we propose a probabilistic approach for
identifying the Purkinje network from non-invasive clinical data such as the
standard electrocardiogram (ECG). We use cardiac imaging to build an
anatomically accurate model of the ventricles; we algorithmically generate a
rule-based Purkinje network tailored to the anatomy; we simulate physiological
electrocardiograms with a fast model; we identify the geometrical and
electrical parameters of the Purkinje-ECG model with Bayesian optimization and
approximate Bayesian computation. The proposed approach is inherently
probabilistic and generates a population of plausible Purkinje networks, all
fitting the ECG within a given tolerance. In this way, we can estimate the
uncertainty of the parameters, thus providing reliable predictions. We test our
methodology in physiological and pathological scenarios, showing that we are
able to accurately recover the ECG with our model. We propagate the uncertainty
in the Purkinje network parameters in a simulation of conduction system pacing
therapy. Our methodology is a step forward in creation of digital twins from
non-invasive data in precision medicine. An open source implementation can be
found at this http URL

从非侵入性临床数据中，利用概率方法识别浦肯野网络，以构建精确的心脏数字孪生模型，为精准心脏学提供可靠预测和不确定性参数估计。

基于心电图的浦肯野网络的概率学习

Probabilistic learning of the Purkinje network from the  electrocardiogram

Myocardial infarction (MI) demands precise and swift diagnosis. Cardiac
digital twins (CDTs) have the potential to offer individualized evaluation of
cardiac function in a non-invasive manner, making them a promising approach for
personalized diagnosis and treatment planning of MI. The inference of accurate
myocardial tissue properties is crucial in creating a reliable CDT platform,
and particularly in the context of studying MI. In this work, we investigate
the feasibility of inferring myocardial tissue properties from the
electrocardiogram (ECG), focusing on the development of a comprehensive CDT
platform specifically designed for MI. The platform integrates multi-modal
data, such as cardiac MRI and ECG, to enhance the accuracy and reliability of
the inferred tissue properties. We perform a sensitivity analysis based on
computer simulations, systematically exploring the effects of infarct location,
size, degree of transmurality, and electrical activity alteration on the
simulated QRS complex of ECG, to establish the limits of the approach. We
subsequently propose a deep computational model to infer infarct location and
distribution from the simulated QRS. The in silico experimental results show
that our model can effectively capture the complex relationships between the
QRS signals and the corresponding infarct regions, with promising potential for
clinical application in the future. The code will be released publicly once the
manuscript is accepted for publication.

该研究探讨了利用心电图推断心肌组织特性和开发面向心肌梗死的全面心脏数字模型的可行性，结果表明该模型能够有效捕捉 QRS 信号和相应梗死区域之间的复杂关系，未来在临床应用中具有巨大潜力。