Deep learning for automated left ventricular outflow tract diameter measurements in 2D echocardiography.

ABSTRACT:

Background

Measurement of the left ventricular outflow tract diameter (LVOTd) in echocardiography is a common source of error when used to calculate the stroke volume. The aim of this study is to assess whether a deep learning (DL) model, trained on a clinical echocardiographic dataset, can perform automatic LVOTd measurements on par with expert cardiologists.

Methods

Data consisted of 649 consecutive transthoracic echocardiographic examinations of patients with coronary artery disease admitted to a university hospital. 1304 LVOTd measurements in the parasternal long axis (PLAX) and zoomed parasternal long axis views (ZPLAX) were collected, with each patient having 1-6 measurements per examination. Data quality control was performed by an expert cardiologist, and spatial geo

SUBMITTER: Zha SZ 

PROVIDER: S-EPMC10571406 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature