Deep Computational Model for the Inference of Ventricular Activation Properties
This work addresses the need for more efficient and accurate patient-specific cardiac models to improve diagnosis and therapy stratification in precision medicine, though it appears incremental as it builds on existing computational methods.
The authors tackled the inefficiency and inaccuracy in creating patient-specific cardiac digital twins by proposing a deep learning model that fuses anatomical and electrophysiological data to infer ventricular activation properties, achieving promising results with fast computational times on simulated data.
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.