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.

翻译：心肌梗死（MI）需要精确且快速的诊断。心脏数字孪生（CDT）能以非侵入方式提供个体化心功能评估，使其成为MI个性化诊断与治疗规划的极具前景的方法。准确推断心肌组织特性对于构建可靠的CDT平台至关重要，尤其在研究MI的背景下。本研究探究从心电图（ECG）推断心肌组织特性的可行性，重点开发针对MI的综合性CDT平台。该平台整合心脏MRI与ECG等多模态数据，以提升推断组织特性的精度与可靠性。我们基于计算机模拟进行敏感性分析，系统探索梗死位置、大小、透壁程度及电活动改变对模拟ECG中QRS波的影响，以确定该方法的边界条件。随后，我们提出一个深度计算模型，从模拟QRS波中推断梗死位置与分布。在虚拟仿真实验中，该模型能有效捕捉QRS信号与相应梗死区域之间的复杂关系，展现出未来临床应用的潜力。论文被接收发表后，相关代码将公开发布。