An Automated View Classification Model for Pediatric Echocardiography Using Artificial Intelligence.

ABSTRACT:

Background

View classification is a key step toward building a fully automated system for interpretation of echocardiograms. However, compared with adult echocardiograms, creating a view classification model for pediatric echocardiograms poses additional challenges, such as greater variation in anatomy, structure size, and views. The aim of this study was to develop a computer vision model to autonomously perform view classification on pediatric echocardiographic images.

Methods

Using a training set of 12,067 echocardiographic images from patients aged 0 to 19 years, a convolutional neural network model was trained to identify 27 preselected standard pediatric echocardiographic views which included anatomic sweeps, color Doppler, and Doppler tracings. A validation set of 6,1

SUBMITTER: Gearhart A 

PROVIDER: S-EPMC9990955 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature