Abstract Congenital heart disease (CHD) is the most common and deadly birth defect, responsible for approximately 11% of infant mortality globally1 Approximately eight in 1,000 infants have CHD, while critical forms of CHD, or life-threatening CCHD, impact roughly two to four of every 1,000 births. Ultrasound is a proven, safe, and effective tool for diagnosing and assessing congenital heart defects (CHDs) and other pediatric heart conditions. Despite this, over 90% of young children lack access to pediatric ultrasound or echocardiography services in the places they are born or seen for routine care. Frontline clinicians in community and rural settings have limited tools to diagnose and manage life-threatening conditions in infants. By integrating ultrasound hardware components in a novel configuration and pairing with machine learning software, the RAPIDscan multi-model imaging device automates the image acquisition process with a repeatable, 30-second mechanical actuation “sweep” that simulates expert image capture from a single subcostal window placement to allow any level of user to screen and identify at-risk patients in minutes.
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