AI-enhanced prognostication in cardiac resynchronization therapy using displacement encoding with stimulated echoes (DENSE) MRI

Synopsis

Keywords: Diagnosis/Prediction, Machine Learning/Artificial Intelligence

Motivation: The need to address the challenge of a high nonresponse rate (approximately 40%) in CRT patients.

Goal(s): By leveraging advanced computational methods, this research seeks to redefine risk stratification and long-term survival prediction.

Approach:

• 3D-CAE model is designed to compress displacement trajectories into a low-dimensional latent code while preserving sufficient information for trajectory reconstruction.
• The survival network utilizes latent features from three specific slices for predicting 4-year survival of patients post-CRT.

Results:

• 3D-CAE model effectively learned to extract latent features and reconstructed displacements with EPE of 0.0914.
• The survival network had the average AUC value for the ROC curves of 0.76 ± 0.04

Impact: This study used important features in myocardial displacement fields. It gives better AUC in comparison with human-derived descriptors of cardiac motion. Also, there are other parameters that can be added to this model to get a promising 4-year survival prediction.