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

TL;DR

This paper presents a novel deep learning approach to infer myocardial infarction tissue properties from ECG data within a cardiac digital twin framework, combining multi-modal data and sensitivity analysis for improved accuracy.

Contribution

It introduces a dual-branch variational autoencoder model for inferring infarct location and distribution from ECG, advancing personalized cardiac diagnostics.

Findings

Achieved mean Dice scores of 0.457 for infarct inference

Demonstrated the model's effectiveness in capturing infarct-electrophysiology relationships

Enhanced understanding of infarct characteristics' impact on ECG features

Abstract

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.