A New Method Using Deep Learning to Predict the Response to Cardiac Resynchronization Therapy.

ABSTRACT: Clinical parameters measured from gated single-photon emission computed tomography myocardial perfusion imaging (SPECT MPI) have value in predicting cardiac resynchronization therapy (CRT) patient outcomes, but still show limitations. The purpose of this study is to combine clinical variables, features from electrocardiogram (ECG), and parameters from assessment of cardiac function with polar maps from gated SPECT MPI through deep learning (DL) to predict CRT response. A total of 218 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 a 6-month follow-up. A DL model was constructed by combining a pre-trained VGG16 model and a multilayer perceptron. Two modalities of data were i

SUBMITTER: Larsen K 

PROVIDER: S-EPMC12701114 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature