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。