Fetal electrocardiography and artificial intelligence for prenatal detection of congenital heart disease.

ABSTRACT:

Introduction

This study aims to investigate non-invasive electrocardiography as a method for the detection of congenital heart disease (CHD) with the help of artificial intelligence.

Material and methods

An artificial neural network was trained for the identification of CHD using non-invasively obtained fetal electrocardiograms. With the help of a Bayesian updating rule, multiple electrocardiographs were used to increase the algorithm's performance.

Results

Using 122 measurements containing 65 healthy and 57 CHD cases, the accuracy, sensitivity, and specificity were found to be 71%, 63%, and 77%, respectively. The sensitivity was however 75% and 69% for CHD cases requiring an intervention in the neonatal period and first year of life, respectively. Furthermore, a posi

SUBMITTER: de Vries IR 

PROVIDER: S-EPMC10577634 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature