Deep Computational Model for the Inference of Ventricular Activation Properties

TL;DR

This paper presents a deep learning-based computational model that integrates anatomical and electrophysiological data to accurately infer ventricular activation properties, aiding personalized cardiac diagnosis and intervention planning.

Contribution

It introduces a novel deep learning approach that fuses anatomical and electrophysiological information for efficient, robust inference of ventricular activation properties from clinical data.

Findings

Model achieves accurate inference of conduction velocities and root nodes.

The approach provides fast computational times suitable for clinical use.

Promising results on simulated data demonstrate potential for real-world application.

Abstract

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.