# Towards Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference arXiv: https://arxiv.org/abs/2307.04421 · HTML: https://mlsanity.com/paper/2307.04421/ ## Key facts - Cited by 18 papers in the map (data through 14 Oct 2026). - Builds on 18 of its 79 references that are in the map. - First posted on arXiv 10 Jul 2023. ## Builds on (most cited first, 18 of 18) - [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021) - [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017) - [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021) - [MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images](https://mlsanity.com/paper/2201.03186/) (2022) - [MyoPS-Net: Myocardial Pathology Segmentation with Flexible Combination of Multi-Sequence CMR Images](https://mlsanity.com/paper/2211.03062/) (2022) - [Multi-Modality Cardiac Image Computing: A Survey](https://mlsanity.com/paper/2208.12881/) (2022) - [Physics-informed neural networks to learn cardiac fiber orientation from multiple electroanatomical maps](https://mlsanity.com/paper/2201.12362/) (2022) - [Physics-constrained Deep Learning for Robust Inverse ECG Modeling](https://mlsanity.com/paper/2107.12780/) (2021) - [AWSnet: An Auto-weighted Supervision Attention Network for Myocardial Scar and Edema Segmentation in Multi-sequence Cardiac Magnetic Resonance Images](https://mlsanity.com/paper/2201.05344/) (2022) - [Deep Computational Model for the Inference of Ventricular Activation Properties](https://mlsanity.com/paper/2208.04028/) (2022) - [Multi-modal Variational Autoencoders for normative modelling across multiple imaging modalities](https://mlsanity.com/paper/2303.12706/) (2023) - [Inference of ventricular activation properties from non-invasive electrocardiography](https://mlsanity.com/paper/2010.15214/) (2020) - [Cobiveco: Consistent biventricular coordinates for precise and intuitive description of position in the heart -- with MATLAB implementation](https://mlsanity.com/paper/2102.02898/) (2021) - [Aligning Multi-Sequence CMR Towards Fully Automated Myocardial Pathology Segmentation](https://mlsanity.com/paper/2302.03537/) (2023) - [Generative Modeling and Inverse Imaging of Cardiac Transmembrane Potential](https://mlsanity.com/paper/1905.04803/) (2019) - [Graph convolutional regression of cardiac depolarization from sparse endocardial maps](https://mlsanity.com/paper/2009.14068/) (2020) - [Reducing Line-of-block Artifacts in Cardiac Activation Maps Estimated Using ECG Imaging: A Comparison of Source Models and Estimation Methods](https://mlsanity.com/paper/2108.06602/) (2021) - [Influence of Myocardial Infarction on QRS Properties: A Simulation Study](https://mlsanity.com/paper/2304.01796/) (2023) ## Built on by (most cited first, 18 of 18) - [Solving the Inverse Problem of Electrocardiography for Cardiac Digital Twins: A Survey](https://mlsanity.com/paper/2406.11445/) (2024) - [Contrast-Free Myocardial Scar Segmentation in Cine MRI using Motion and Texture Fusion](https://mlsanity.com/paper/2501.05241/) (2025) - [Personalized 3D Myocardial Infarct Geometry Reconstruction from Cine MRI with Explicit Cardiac Motion Modeling](https://mlsanity.com/paper/2507.15194/) (2025) - [CineMesh4D: Personalized 4D Whole Heart Reconstruction from Sparse Cine MRI](https://mlsanity.com/paper/2605.13994/) (2026) - [Personalized Topology-Informed Localization of Standard 12-Lead ECG Electrode Placement from Incomplete Cardiac MRIs for Efficient Cardiac Digital Twins](https://mlsanity.com/paper/2408.13945/) (2024) - [UltraTwin: Towards Cardiac Anatomical Twin Generation from Multi-view 2D Ultrasound](https://mlsanity.com/paper/2506.23490/) (2025) - [Multimodal Variational Autoencoder for Low-cost Cardiac Hemodynamics Instability Detection](https://mlsanity.com/paper/2403.13658/) (2024) - [ECGDeDRDNet: A deep learning-based method for Electrocardiogram noise removal using a double recurrent dense network](https://mlsanity.com/paper/2505.05477/) (2025) - [LA-CaRe-CNN: Cascading Refinement CNN for Left Atrial Scar Segmentation](https://mlsanity.com/paper/2508.04553/) (2025) - [3D Cardiac Anatomy Generation Using Mesh Latent Diffusion Models](https://mlsanity.com/paper/2508.14122/) (2025) - [Seeing Beyond the Image: ECG and Anatomical Knowledge-Guided Myocardial Scar Segmentation from Late Gadolinium-Enhanced Images](https://mlsanity.com/paper/2511.14702/) (2025) - [Personalized 4D Whole-Heart Mesh Reconstruction from Cine MRI via Multi-Scale Temporal Modeling and Differentiable Contour Rendering](https://mlsanity.com/paper/2607.01952/) (2026) - [Personalized 4D Whole Heart Geometry Reconstruction from Cine MRI for Cardiac Digital Twins](https://mlsanity.com/paper/2507.15203/) (2025) - [TF-TransUNet1D: Time-Frequency Guided Transformer U-Net for Robust ECG Denoising in Digital Twin](https://mlsanity.com/paper/2508.20398/) (2025) - [Chain of Flow: ECG-Conditioned 4D Cardiac Cine Generation from Patient-Specific Anatomical Anchor](https://mlsanity.com/paper/2602.22919/) (2026) - [Physiology and Anatomy Aware Inverse Inference of Myocardial Infarction for Cardiac Digital Twin](https://mlsanity.com/paper/2605.22044/) (2026) - [Echo4DIR: 4D Implicit Heart Reconstruction from 2D Echocardiography Videos](https://mlsanity.com/paper/2605.22066/) (2026) - [Anatomy-Aligned Surface Field Learning for Myocardial Reconstruction from Sparse Short-Axis Cine MRI](https://mlsanity.com/paper/2609.29825/) (2026) ## Abstract (arXiv) 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 ± 0.317 and 0.302 ± 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.