Artificial intelligence based automatic classification, annotation, and measurement of the fetal heart using HeartAssist.

ABSTRACT: This study evaluated the feasibility of HeartAssist, a novel automated tool designed for classification of fetal cardiac views, annotation of cardiac structures, and measurement of cardiac parameters. Unlike previous AI tools that primarily focused on classification, HeartAssist integrates classification, annotation and measurement capabilities, enabling a more comprehensive fetal cardiac assessment.Cardiac images from fetuses (gestational ages 20-40 weeks) were collected at Asan Medical Center between January 2016 and October 2018. HeartAssist was developed using convolutional neural networks to classify 10 cardiac views, annotate 26 structures, and measure 43 parameters. One expert performed manual classifications, annotations, and measurements, which were then compared to HeartAssist ou

SUBMITTER: Kim R 

PROVIDER: S-EPMC12003710 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature