Abstract

Asymptomatic left ventricular dysfunction (ALVD) is present in 3–6% of the general population, is associated with reduced quality of life and longevity, and is treatable when found1,2,3,4. An inexpensive, noninvasive screening tool for ALVD in the doctor’s office is not available. We tested the hypothesis that application of artificial intelligence (AI) to the electrocardiogram (ECG), a routine method of measuring the heart’s electrical activity, could identify ALVD. Using paired 12-lead ECG and echocardiogram data, including the left ventricular ejection fraction (a measure of contractile function), from 44,959 patients at the Mayo Clinic, we trained a convolutional neural network to identify patients with ventricular dysfunction, defined as ejection fraction ≤35%, using the ECG data alone. When tested on an independent set of 52,870 patients, the network model yielded values for the area under the curve, sensitivity, specificity, and accuracy of 0.93, 86.3%, 85.7%, and 85.7%, respectively. In patients without ventricular dysfunction, those with a positive AI screen were at 4 times the risk (hazard ratio, 4.1; 95% confidence interval, 3.3 to 5.0) of developing future ventricular dysfunction compared with those with a negative screen. Application of AI to the ECG—a ubiquitous, low-cost test—permits the ECG to serve as a powerful screening tool in asymptomatic individuals to identify ALVD.

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Acknowledgements

The study was conceived, funded, and executed entirely by Mayo Clinic. There was no industry support of any kind.

Author information

Affiliations

  1. Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA

    • Zachi I. Attia
    • , Suraj Kapa
    • , Francisco Lopez-Jimenez
    • , Paul M. McKie
    • , Patricia A. Pellikka
    • , Maurice Enriquez-Sarano
    • , Peter A. Noseworthy
    • , Thomas M. Munger
    • , Samuel J. Asirvatham
    •  & Paul A. Friedman
  2. Business Development, Mayo Clinic, Rochester, MN, USA

    • Dorothy J. Ladewig
    •  & Gaurav Satam
  3. Health Sciences Research, Mayo Clinic, Rochester, MN, USA

    • Christopher G. Scott
  4. Health Sciences Research, Mayo Clinic, Jacksonville, FL, USA

    • Rickey E. Carter

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Contributions

D.J.L. and G.S. contributed to the literature search, study coordination, data management, and data collection. Z.I.A., P.A.F., S.K., and F.L.-J. contributed to the study design. C.G.S., Z.I.A., R.E.C., and F.L.-J. contributed to the data analysis. C.G.S., Z.I.A., R.E.C., P.A.N., P.A.P., M.E.-S., P.A.F., S.K., P.M.M., T.M.M., and S.J.A. contributed to data interpretation. Z.I.A., R.E.C., F.L.-J., P.A.F., P.A.N., and P.A.P. contributed to the writing of the manuscript. R.E.C., P.A.N., P.A.P., M.E.-S., F.L.-J., S.K., P.M.M., T.M.M., and S.J.A contributed to the critical review and editing.

Competing interests

Mayo Clinic has licensed the underlying technology to EKO, a maker of digital stethoscopes with embedded ECG electrodes. Mayo Clinic may receive financial benefit from the use of this technology, but at no point will Mayo Clinic benefit financially from its use for the care of patients at Mayo Clinic. P.A.F., F.L.-J., S.K., and Z.I.A. may also receive financial benefit from this agreement.

Corresponding author

Correspondence to Paul A. Friedman.

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DOI

https://doi.org/10.1038/s41591-018-0240-2

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