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：心肌梗死需要精准快速的诊断。心脏数字孪生有望以非侵入性方式提供心脏功能的个性化评估，使其成为心肌梗死个性化诊断与治疗规划中有前景的方法。准确推断心肌组织特性对于构建可靠的心脏数字孪生平台至关重要，尤其是在研究心肌梗死这一背景下。本研究探究了从心电图推断心肌组织特性的可行性，重点开发了一个专门针对心肌梗死的综合性心脏数字孪生平台。该平台整合了心脏磁共振成像和心电图等多模态数据，以提高所推断组织特性的准确性与可靠性。我们基于计算机模拟进行了敏感性分析，系统探究了梗死位置、大小、透壁程度以及电生理活动改变对模拟心电QRS波群的影响，以明确该方法的适用范围。随后，我们提出了一种深度计算模型，用于从模拟QRS波群中推断梗死位置和分布。计算机模拟实验结果表明，该模型能有效捕捉QRS信号与对应梗死区域之间的复杂关系，未来在临床应用中具有潜力。相关代码将在论文被接收发表后公开发布。