ECG-enabled stethoscopes (ECG-steth) can acquire single lead ECGs during cardiac auscultation, and may facilitate real-time screening for pathologies not routinely identified during physical examination (eg, arrhythmias). We previously demonstrated an artificial intelligence (AI) algorithm applied to a 12-lead ECG (ECG-12) can identify low ejection fraction (EF) (defined as <35%) with an accuracy of 87%. It is unknown if AI algorithms trained from ECG-12 can be applied to single lead ECGs acquired through devices such as ECG-steth. To demonstrate that an AI algorithm trained using ECG-12 can be applied to ECG-steth for detection of low EF.

Prospective Analysis of Utility of Signals from an ECG-enabled Stethoscope to Automatically Detect a Low Ejection Fraction using neural networks trained from the standard 12-lead ECG