Artificial Intelligence-Enabled ECG: a Modern Lens on an Old Technology

Abstract

Purpose of Review

To (i) review the concept of artificial intelligence (AI); (ii) summarize recent developments in artificial intelligence-enabled electrocardiogram (AI-ECG); (iii) address notable inherent limitations and challenges of AI-ECG; and (iv) discuss the future direction of the field.

Recent Findings

Advancements in machine learning and computing methods have led to application of AI-ECG and potential new applications to patient care. Further study is needed to verify previous findings in diverse populations as well as begin to confront the limitations needed for clinical implementation.

Summary

Nearly one century after the Nobel Prize was awarded to Willem Einthoven for demonstrating that an electrocardiogram (ECG) could record the electrical signature of the heart, the ECG remains one of the most important diagnostic tests in modern medicine. We now stand at the edge of true ECG innovation. Simultaneous advancements in computing power, wireless technology, digitized data availability, and machine learning have led to the birth of AI-ECG algorithms with novel capabilities and real potential for clinical application. AI has the potential to improve diagnostic accuracy and efficiency by providing fully automated, unbiased, and unambiguous ECG analysis along with promising new findings that may unlock new value in the ECG. These breakthroughs may cause a paradigm shift in clinical workflow as well as patient monitoring and management.

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Correspondence to Peter A. Noseworthy.

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Conflict of Interest

Anthony H. Kashou and Adam M. May declare that they have no conflict of interest. Peter A. Noseworthy has a potential equity financial interest in AliveCor although he has received no payments from Alivecor. In addition, Dr. Noseworthy has two pending patents (US2019/033678 and 62/751,395).

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Kashou, A.H., May, A.M. & Noseworthy, P.A. Artificial Intelligence-Enabled ECG: a Modern Lens on an Old Technology. Curr Cardiol Rep 22, 57 (2020). https://doi.org/10.1007/s11886-020-01317-x

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Keywords

  • Electrocardiogram
  • Artificial intelligence
  • Machine learning
  • Deep learning
  • Convolutional neural network