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Myocardial infarction (MI) demands precise and swift diagnosis. Cardiacdigital twins (CDTs) have the potential to offer individualized evaluation ofcardiac function in a non-invasive manner, making them a promising approach forpersonalized diagnosis and treatment planning of MI. The inference of accuratemyocardial tissue properties is crucial in creating a reliable CDT platform,and particularly in the context of studying MI. In this work, we investigatethe feasibility of inferring myocardial tissue properties from theelectrocardiogram (ECG), focusing on the development of a comprehensive CDTplatform specifically designed for MI. The platform integrates multi-modaldata, such as cardiac MRI and ECG, to enhance the accuracy and reliability ofthe inferred tissue properties. We perform a sensitivity analysis based oncomputer simulations, systematically exploring the effects of infarct location,size, degree of transmurality, and electrical activity alteration on thesimulated QRS complex of ECG, to establish the limits of the approach. Wesubsequently propose a deep computational model to infer infarct location anddistribution from the simulated QRS. The in silico experimental results showthat our model can effectively capture the complex relationships between theQRS signals and the corresponding infarct regions, with promising potential forclinical application in the future. The code will be released publicly once themanuscript is accepted for publication.

Towards Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference