  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
my-ocardial infarction (MI). The inference of accurate myocardial tissue
properties is crucial in creating a reliable CDT of MI. In this work, we
investigate the feasibility of inferring myocardial tissue properties from the
electrocardiogram (ECG) within a CDT platform. 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 ac-tivity
alteration on the simulated QRS complex of ECG, to establish the limits of the
approach. We subsequently present a novel deep computational model, comprising
a dual-branch variational autoencoder and an inference model, to infer infarct
location and distribution from the simulated QRS. The proposed model achieves
mean Dice scores of 0.457 \pm 0.317 and 0.302 \pm 0.273 for the inference of
left ventricle scars and border zone, respectively. The sensitivity analysis
enhances our understanding of the complex relationship between infarct
characteristics and electrophysiological features. The in silico experimental
results show that the model can effectively capture the relationship for the
inverse inference, with promising potential for clinical application in the
future. The code will be released publicly once the manuscript is accepted for
publication.
