2023
DOI: 10.3390/jcdd10040175
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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 … Show more

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Cited by 30 publications
(19 citation statements)
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“…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 .…”
Section: Discussionmentioning
confidence: 99%
“…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 .…”
Section: Discussionmentioning
confidence: 99%
“…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].…”
Section: Zusatzinformationunclassified
“…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.…”
Section: Introductionmentioning
confidence: 99%