Current and Future Use of Artificial Intelligence in Electrocardiography

Abstract

Artificial intelligence (AI) is increasingly used in electrocardiography (ECG) to assist in diagnosis, stratification, and management. AI algorithms can help clinicians in the following areas: (1) interpretation and detection of arrhythmias, ST-segment changes, QT prolongation, and other ECG abnormalities; (2) risk prediction integrated with or without clinical variables (to predict arrhythmias, sudden cardiac death, stroke, and other cardiovascular events); (3) monitoring ECG signals from cardiac implantable electronic devices and wearable devices in real time and alerting clinicians or patients when significant changes occur according to timing, duration, and situation; (4) signal processing, improving ECG quality and accuracy by removing noise/artifacts/interference, and extracting features not visible to the human eye (heart rate variability, beat-to-beat intervals, wavelet transforms, sample-level resolution, etc.); (5) therapy guidance, assisting in patient selection, optimizing treatments, improving symptom-to-treatment times, and cost effectiveness (earlier activation of code infarction in patients with ST-segment elevation, predicting the response to antiarrhythmic drugs or cardiac implantable devices therapies, reducing the risk of cardiac toxicity, etc.); (6) facilitating the integration of ECG data with other modalities (imaging, genomics, proteomics, biomarkers, etc.). In the future, AI is expected to play an increasingly important role in ECG diagnosis and management, as more data become available and more sophisticated algorithms are developed.

The prognostic value of artificial intelligence to predict cardiac amyloidosis in patients with severe aortic stenosis undergoing transcatheter aortic valve replacement

“…Advances in AI have created revolutionary and unique opportunities to predict cardiovascular pathology from tests that may appear normal to the human eye. 20 , 21 The first such development with ECG was a convolutional neural network model to detect the electrocardiographic signature and predict atrial fibrillation development from ECGs in normal sinus rhythm. 22 , 23 Similarly, Grogan et al .…”

Diagnose des akuten Koronarsyndroms

“…Viele Geräte bieten bereits automatisierte EKG-Auswertungen, die als Unterstützung genutzt werden können, die ärztliche Beurteilung allerdings nicht ersetzen. Gerade durch Nutzung künstlicher Intelligenz besteht allerdings großes Potenzial, die Genauigkeit dieser automatisierten Auswertung deutlich zu steigern [12].…”

Application of Convolutional Neural Network for Decoding of 12-Lead Electrocardiogram from a Frequency-Modulated Audio Stream (Sonified ECG)

“…Improving conventional diagnostic methods in clinical practice relies on the development of new artificial intelligence (AI) technologies for interpretation, risk prediction, real-time monitoring, improved noise immunity, therapy guidance, and facilitated integrations of numerous known and novel modalities for presenting physiologic signals—electrocardiogram (ECG), electromyogram (EMG), encephalogram (EEG), etc. [ 1 , 2 ]. AI is expected to play an increasingly important role in the diagnosis and management process as more data become available and more sophisticated algorithms are developed.…”