Leveraging ECG images for predicting ejection fraction using machine learning algorithms.

ABSTRACT:

Introduction

The capability to accurately predict the ejection fraction (EF) from an electrocardiogram (ECG) holds significant and valuable clinical implications. Various algorithms based on ECG images are currently being evaluated, with most methods requiring raw signal data from ECG devices. In this study, our objective was to train and validate a neural network on a readily available ECG trace image graph to determine the presence or absence of left ventricular dysfunction (LVD).

Methods

12-lead ECG trace images paired with their echocardiogram reports performed on the same day were selected. A DenseNet121 model, using ECG images as input, was trained to identify EF <50 %. and then externally validated.

Results

1,19,281 ECG-echocardiogram pairs were used for model

SUBMITTER: Swamy AK 

PROVIDER: S-EPMC12223524 | biostudies-literature | 2025 May-Jun

REPOSITORIES: biostudies-literature