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- Sulaiman Somani
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA
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- Adam J Russak
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA
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- Felix Richter
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA
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- Shan Zhao
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA
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- Akhil Vaid
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA
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- Fayzan Chaudhry
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA
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- Jessica K De Freitas
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA
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- Nidhi Naik
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA
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- Riccardo Miotto
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA
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- Girish N Nadkarni
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA
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- Jagat Narula
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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- Edgar Argulian
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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- Benjamin S Glicksberg
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA
説明
<jats:title>Abstract</jats:title> <jats:p>In the recent decade, deep learning, a subset of artificial intelligence and machine learning, has been used to identify patterns in big healthcare datasets for disease phenotyping, event predictions, and complex decision making. Public datasets for electrocardiograms (ECGs) have existed since the 1980s and have been used for very specific tasks in cardiology, such as arrhythmia, ischemia, and cardiomyopathy detection. Recently, private institutions have begun curating large ECG databases that are orders of magnitude larger than the public databases for ingestion by deep learning models. These efforts have demonstrated not only improved performance and generalizability in these aforementioned tasks but also application to novel clinical scenarios. This review focuses on orienting the clinician towards fundamental tenets of deep learning, state-of-the-art prior to its use for ECG analysis, and current applications of deep learning on ECGs, as well as their limitations and future areas of improvement.</jats:p>
収録刊行物
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- EP Europace
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EP Europace 23 (8), 1179-1191, 2021-02-10
Oxford University Press (OUP)
