A method using deep learning to discover new predictors from left-ventricular mechanical dyssynchrony for CRT response.

ABSTRACT:

Background

Studies have shown that the conventional parameters characterizing left ventricular mechanical dyssynchrony (LVMD) measured on gated SPECT myocardial perfusion imaging (MPI) have their own statistical limitations in predicting cardiac resynchronization therapy (CRT) response. The purpose of this study is to discover new predictors from the polarmaps of LVMD by deep learning to help select heart failure patients with a high likelihood of response to CRT.

Methods

One hundred and fifty-seven patients who underwent rest gated SPECT MPI were enrolled in this study. CRT response was defined as an increase in left ventricular ejection fraction (LVEF) > 5% at 6 [Formula: see text] 1 month follow up. The autoencoder (AE) technique, an unsupervised deep learning method, was

SUBMITTER: He Z 

PROVIDER: S-EPMC10961110 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature