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Artificial Intelligence Interpretation of the Electrocardiogram: A State-of-the-Art Review

  • Invasive Electrophysiology and Pacing (EK Heist, Section Editor)
  • Published:
Current Cardiology Reports Aims and scope Submit manuscript

Abstract

Purpose of Review

Artificial intelligence (AI) is transforming electrocardiography (ECG) interpretation. AI diagnostics can reach beyond human capabilities, facilitate automated access to nuanced ECG interpretation, and expand the scope of cardiovascular screening in the population. AI can be applied to the standard 12-lead resting ECG and single-lead ECGs in external monitors, implantable devices, and direct-to-consumer smart devices. We summarize the current state of the literature on AI-ECG.

Recent Findings

Rhythm classification was the first application of AI-ECG. Subsequently, AI-ECG models have been developed for screening structural heart disease including hypertrophic cardiomyopathy, cardiac amyloidosis, aortic stenosis, pulmonary hypertension, and left ventricular systolic dysfunction. Further, AI models can predict future events like development of systolic heart failure and atrial fibrillation. AI-ECG exhibits potential in acute cardiac events and non-cardiac applications, including acute pulmonary embolism, electrolyte abnormalities, monitoring drugs therapy, sleep apnea, and predicting all-cause mortality.

Summary

Many AI models in the domain of cardiac monitors and smart watches have received Food and Drug Administration (FDA) clearance for rhythm classification, while others for identification of cardiac amyloidosis, pulmonary hypertension and left ventricular dysfunction have received breakthrough device designation. As AI-ECG models continue to be developed, in addition to regulatory oversight and monetization challenges, thoughtful clinical implementation to streamline workflows, avoiding information overload and overwhelming of healthcare systems with false positive results is necessary. Research to demonstrate and validate improvement in healthcare efficiency and improved patient outcomes would be required before widespread adoption of any AI-ECG model.

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Authors and Affiliations

  1. The University of Kansas School of Medicine, Kansas City, KS, USA

    Benjamin Ose

  2. Division of General and Hospital Medicine, The University of Kansas Medical Center, Kansas City, KS, USA

    Zeeshan Sattar

  3. Department of Cardiovascular Medicine, The University of Kansas Medical Center, Kansas City, KS, USA

    Amulya Gupta, Chris Harvey & Amit Noheria

  4. Program for AI & Research in Cardiovascular Medicine (PARC), The University of Kansas Medical Center, Kansas City, KS, USA

    Amulya Gupta, Chris Harvey & Amit Noheria

  5. Trinity Health Oakland Hospital, Pontiac, MI, USA

    Christian Toquica

Authors
  1. Benjamin Ose
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  2. Zeeshan Sattar
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  3. Amulya Gupta
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  4. Christian Toquica
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  5. Chris Harvey
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  6. Amit Noheria
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Contributions

B.O., Z.S., C.T., A.G., and A.N. wrote the main manuscript. C.H. prepared table 1. B.O. and Z.S. prepared tables 2-6. A.G. prepared table 7 and figure 1. All authors reviewed the manuscript.

Corresponding author

Correspondence to Amit Noheria.

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Ose, B., Sattar, Z., Gupta, A. et al. Artificial Intelligence Interpretation of the Electrocardiogram: A State-of-the-Art Review. Curr Cardiol Rep (2024). https://doi.org/10.1007/s11886-024-02062-1

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