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 input to the model: polar map images from SPECT MPI and tabular data from clinical features, ECG parameters, and SPECT-MPI-derived parameters. Gradient-weighted class activation mapping (Grad-CAM) was applied to the VGG16 model to provide explainability for the polar maps. For comparison, four machine learning (ML) models were trained using only the tabular features. Modeling was performed on 218 patients who underwent CRT implantation with a response rate of 55.5% (n = 121). The DL model demonstrated average AUC (0.83), accuracy (0.73), sensitivity (0.76), and specificity (0.69) surpassing ML models and guideline criteria. Guideline recommendations achieved accuracy (0.53), sensitivity (0.75), and specificity (0.26). The DL model trended towards improvement over the ML models, showcasing the additional predictive benefit of utilizing SPECT MPI polar maps. Incorporating additional patient data directly in the form of medical imagery can improve CRT response prediction.