Deep Learning for Explainable Estimation of Mortality Risk From Myocardial Positron Emission Tomography Images.

ABSTRACT:

Background

We aim to develop an explainable deep learning (DL) network for the prediction of all-cause mortality directly from positron emission tomography myocardial perfusion imaging flow and perfusion polar map data and evaluate it using prospective testing.

Methods

A total of 4735 consecutive patients referred for stress and rest ⁸²Rb positron emission tomography between 2010 and 2018 were followed up for all-cause mortality for 4.15 (2.24-6.3) years. DL network utilized polar maps of stress and rest perfusion, myocardial blood flow, myocardial flow reserve, and spill-over fraction combined with cardiac volumes, singular indices, and sex. Patients scanned from 2010 to 2016 were used for training and validation. The network was tested in a set of 1135 patients

SUBMITTER: Singh A 

PROVIDER: S-EPMC10035936 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature