Deep learning model for prenatal congenital heart disease (CHD) screening can be applied to retrospective imaging from the community setting, outperforming initial clinical detection in a well-annotated cohort

Abstract

Despite nearly universal prenatal ultrasound screening programs, congenital heart defects (CHD) are still missed, which may result in severe morbidity or even death. Deep machine learning (DL) can automate image recognition from ultrasound. The main aim of this study was to assess the performance of a previously developed DL model, trained on images from a tertiary center, using fetal ultrasound images obtained during the second‐trimester standard anomaly scan in a low‐risk population. A secondary aim was to compare initial screening diagnosis, which made use of live imaging at the point‐of‐care, with diagnosis by clinicians evaluating only stored images.