Echocardiography-based AI detection of regional wall motion abnormalities and quantification of cardiac function in myocardial infarction.

ABSTRACT:

Objective

To compare the performance of a newly developed deep learning (DL) framework for automatic detection of regional wall motion abnormalities (RWMAs) for patients presenting with the suspicion of myocardial infarction from echocardiograms obtained with portable bedside equipment versus standard equipment.

Background

Bedside echocardiography is increasingly used by emergency department setting for rapid triage of patients presenting with chest pain. However, compared to images obtained with standard equipment, lower image quality from bedside equipment can lead to improper diagnosis. To overcome these limitations, we developed an automatic workflow to process echocardiograms, including view selection, segmentation, detection of RWMAs and quantification of cardiac funct

SUBMITTER: Lin X 

PROVIDER: S-EPMC9441592 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature