TheĭL model outperformed the ML models, showcasing the additional predictiveīenefit of utilizing SPECT MPI polarmaps. Guideline recommendations presentedĪccuracy (0.53), sensitivity (0.75), and specificity (0.26). Modeling was performed on 218 patients who underwent CRT implantation Machine learning (ML) models were trained using only the tabular features.
Module to provide explainability for the polarmaps. Gradient-weighted Class Activation Mapping (Grad-CAM) was applied to the VGG16 Two modalities of data were input to the model: polarmap imagesįrom SPECT MPI and tabular data from clinical features and ECG parameters. Model was constructed by combining a pre-trained VGG16 module and a multilayer Left ventricular ejection fraction (LVEF) > 5% at a 6-month follow up. CRT response was defined as an increase in 218 patients who underwent rest gated SPECT
The purpose of this study is to combine clinical variables,įeatures from electrocardiogram (ECG), and parameters from assessment ofĬardiac function with polarmaps from gated SPECT MPI through deep learning (DL) Predicting cardiac resynchronization therapy (CRT) patient outcomes, but still Clinical parameters measured from gated single-photon emissionĬomputed tomography myocardial perfusion imaging (SPECT MPI) have value in Forest Service.Download a PDF of the paper titled A new method using deep learning to predict the response to cardiac resynchronization therapy, by Kristoffer Larsena and 10 other authors Download PDF Abstract: Background. This app is powered by OnCell, an official mobile partner of the U.S. A static area map also helps orient you when connectivity is not available.ĭownloading this app will store its contents on your device, so you can continue to access the tour and information when there is no cell coverage in the forest. A GPS map allows visitors to explore our forest and locate nearby points of interest.
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