Deep learning model for prenatal congenital heart disease (CHD) screening generalizes to the community setting and outperforms clinical detection

Abstract

A previously trained DL algorithm out-performed human experts in detecting CHD in a cohort in which over 50 percent of CHD cases were initially missed clinically. Notably, the DL algorithm performed well on community-acquired images in a low-risk population, including lesions it had not been previously exposed to. Furthermore, when both the model and blinded human experts had access to stored images alone, the model outperformed expert humans. Together, these findings support the proposition that use of DL models can improve prenatal detection of CHD.

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