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
To demonstrate that an AI algorithm trained using ECG-12 can be applied to ECG-steth for detection of low EF.
100 patients referred for echocardiography were included. In addition to transthoracic echocardiogram, ECG-steth with patient supine and/or sitting were obtained in standard positions where cardiac auscultation is done and via a hand-held lead I equivalent (Figure). An AI algorithm trained on 35,970 independent patients with pairs of ECG-12 and echocardiograms was retrained using a single lead from ECG-12 and validated against ECG-steth to determine accuracy for low EF detection (
Amongst 100 patients (age 70.6±13.8; 61% male), 7 had EF
In a prospective study, an AI algorithm reliably detected low EF from single lead ECGs acquired using a novel ECG-enabled stethoscope in standard auscultation positions. The ability to identify patients with a possible low EF during routine physical examination may facilitate rapid clinical recognition of patients requiring further testing such as echocardiography.
Zachi I Attia, Jennifer Dugan, Mayo Clinic, Rochester, MN; John Maidens, Eko, Berkeley, CA; Adam Rideout, Eko, Berekely, CA; Francisco Lopez-Jimenez, Peter Alexander Noseworthy, Samuel Asirvatham, Patricia A Pellikka, Dorothy J Ladewig, Gaurav Satam, Mayo Clinic, Rochester, MN; Steve Pham, Eko, Rochester, MN; Subramaniam Venkatraman, EKO HEALTH, Berkeley, CA; Paul Friedman, Suraj Kapa, Mayo Clinic, Rochester, MN