Automated and accurate view classification of fetal echocardiography using convolutional neural networks.

ABSTRACT:

Background

Accurate view classification is imperative for the prenatal precision diagnosis of congenital heart disease (CHD) but challenges remain, including suboptimal imaging quality due to noise and artifacts, along with a high reliance on extensive operator or diagnostician expertise, which complicates this process. We aimed to analyze several convolutional neural network (CNN)-based frameworks for the automated classification of eight standard fetal echocardiographic views.

Methods

The study utilized fetal echocardiographic data collected from 953 second- and third-trimester fetuses between July 2018 and October 2019. Among them, 10,032 images were randomly selected for training and validation, while 2,071 images were used to assess the framework's accuracy. Various adv

SUBMITTER: Shi J 

PROVIDER: S-EPMC12682528 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature