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Jul 7, 2019

Current and Future Applications in Artificial Intelligence in Cardiology

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

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ECG

Artificial Intelligence

Machine Learning

Deep Learning

Neural Networks

Abstract

Abstract

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Keywords

ECG

Artificial Intelligence

Machine Learning

Deep Learning

Neural Networks

Abstract

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.

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© Copyright 2019 Morressier GmbH. All rights reserved.

© Copyright 2019 Morressier GmbH.
All rights reserved.