# Deep Computational Model for the Inference of Ventricular Activation Properties

arXiv: https://arxiv.org/abs/2208.04028 · HTML: https://mlsanity.com/paper/2208.04028/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 31 references that are in the map.
- First posted on arXiv 8 Aug 2022.

## Builds on (most cited first, 5 of 5)

- [PCN: Point Completion Network](https://mlsanity.com/paper/1808.00671/) (2018)
- [Inference of ventricular activation properties from non-invasive electrocardiography](https://mlsanity.com/paper/2010.15214/) (2020)
- [GEASI: Geodesic-based Earliest Activation Sites Identification in cardiac models](https://mlsanity.com/paper/2102.09962/) (2021)
- [Graph convolutional regression of cardiac depolarization from sparse endocardial maps](https://mlsanity.com/paper/2009.14068/) (2020)
- [Conforming restricted Delaunay mesh generation for piecewise smooth complexes](https://mlsanity.com/paper/1606.01289/) (2016)

## Built on by (most cited first, 10 of 10)

- [Towards Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference](https://mlsanity.com/paper/2307.04421/) (2023)
- [Multi-class point cloud completion networks for 3D cardiac anatomy reconstruction from cine magnetic resonance images](https://mlsanity.com/paper/2307.08535/) (2023)
- [Solving the Inverse Problem of Electrocardiography for Cardiac Digital Twins: A Survey](https://mlsanity.com/paper/2406.11445/) (2024)
- [Large Language Model-informed ECG Dual Attention Network for Heart Failure Risk Prediction](https://mlsanity.com/paper/2403.10581/) (2024)
- [Modeling 3D cardiac contraction and relaxation with point cloud deformation networks](https://mlsanity.com/paper/2307.10927/) (2023)
- [A Conditional Flow Variational Autoencoder for Controllable Synthesis of Virtual Populations of Anatomy](https://mlsanity.com/paper/2306.14680/) (2023)
- [Digital twinning of cardiac electrophysiology models from the surface ECG: a geodesic backpropagation approach](https://mlsanity.com/paper/2308.08410/) (2023)
- [3D Shape-Based Myocardial Infarction Prediction Using Point Cloud Classification Networks](https://mlsanity.com/paper/2307.07298/) (2023)
- [Influence of Myocardial Infarction on QRS Properties: A Simulation Study](https://mlsanity.com/paper/2304.01796/) (2023)
- [3D Cardiac Anatomy Generation Using Mesh Latent Diffusion Models](https://mlsanity.com/paper/2508.14122/) (2025)

## Abstract (arXiv)

Patient-specific cardiac computational models are essential for the efficient realization of precision medicine and in-silico clinical trials using digital twins. Cardiac digital twins can provide non-invasive characterizations of cardiac functions for individual patients, and therefore are promising for the patient-specific diagnosis and therapy stratification. However, current workflows for both the anatomical and functional twinning phases, referring to the inference of model anatomy and parameter from clinical data, are not sufficiently efficient, robust, and accurate. In this work, we propose a deep learning based patient-specific computational model, which can fuse both anatomical and electrophysiological information for the inference of ventricular activation properties, i.e., conduction velocities and root nodes. The activation properties can provide a quantitative assessment of cardiac electrophysiological function for the guidance of interventional procedures. We employ the Eikonal model to generate simulated electrocardiogram (ECG) with ground truth properties to train the inference model, where specific patient information has also been considered. For evaluation, we test the model on the simulated data and obtain generally promising results with fast computational time.