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 outputs to assess feasibility. A total of 65,324 images from 2,985 fetuses were analyzed. HeartAssist achieved 99.4% classification accuracy, with recall, precision, and F1-score of 0.93, 0.95, and 0.94, respectively. Annotation accuracy was 98.4%, while the automatic measurement success rate was 97.6%, with an error rate of 7.62% and caliper similarity of 0.613. HeartAssist is a reliable tool for fetal cardiac screening, demonstrating high accuracy in classifying cardiac views and annotating structures, with comparable outcomes in measuring cardiac parameters. This tool could enhance prenatal detection of congenital heart disease and improve perinatal outcomes.