Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram

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.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Creation of the study data sets.
Fig. 2: Network ROC and sensitivity and specificity across age and gender subsets.
Fig. 3: Long-term incidence of developing an EF of ≤35% in patients with an initially normal EF stratified by AI classification.

Data availability

All requests for raw and analyzed data and related materials, excluding programming code, will be reviewed by the Mayo Clinic legal department and Mayo Clinic Ventures to verify whether the request is subject to any intellectual property or confidentiality obligations. Requests for patient-related data not included in the paper will not be considered. Any data and materials that can be shared will be released via a Material Transfer Agreement.

References

  1. McDonagh, T. A., McDonald, K. & Maisel, A. S. Screening for asymptomatic left ventricular dysfunction using B-type natriuretic Peptide. Congest. Heart Fail. 14, 5–8 (2008).

    Article  CAS  Google Scholar 

  2. Dargie, H. J. Effect of carvedilol on outcome after myocardial infarction in patients with left-ventricular dysfunction: the CAPRICORN randomised trial. Lancet 357, 1385–1390 (2001).

    Article  CAS  Google Scholar 

  3. Pfeffer, M. A. et al. Effect of captopril on mortality and morbidity in patients with left ventricular dysfunction after myocardial infarction. Results of the survival and ventricular enlargement trial. N. Engl. J. Med. 327, 669–677 (1992).

    Article  CAS  Google Scholar 

  4. Priori, S. G. et al. 2015 ESC guidelines for the management of patients with ventricular arrhythmias and the prevention of sudden cardiac death: the Task Force for the Management of Patients with Ventricular Arrhythmias and the Prevention of Sudden Cardiac Death of the European Society of Cardiology (ESC). Endorsed by: Association for European Paediatric and Congenital Cardiology (AEPC). Eur. Heart J. 36, 2793–2867 (2015).

    Article  Google Scholar 

  5. Betti, I. et al. The role of N-terminal PRO-brain natriuretic peptide and echocardiography for screening asymptomatic left ventricular dysfunction in a population at high risk for heart failure. The PROBE-HF study. J. Card. Fail. 15, 377–384 (2009).

    Article  CAS  Google Scholar 

  6. Redfield, M. M. et al. Plasma brain natriuretic peptide to detect preclinical ventricular systolic or diastolic dysfunction: a community-based study. Circulation 109, 3176–3181 (2004).

    Article  CAS  Google Scholar 

  7. Kim, J. H., Kwon, H. S. & Seo, H. W. Evaluating a pivot-based approach for bilingual lexicon extraction. Comput. Intell. Neurosci. 2015, 434153 (2015).

    Article  Google Scholar 

  8. Pasquier, M., Quek, C. & Toh, M. Fuzzylot: a novel self-organising fuzzy-neural rule-based pilot system for automated vehicles. Neural Netw. 14, 1099–1112 (2001).

    Article  CAS  Google Scholar 

  9. Salazar-Licea, L. A., Pedraza-Ortega, J. C., Pastrana-Palma, A. & Aceves-Fernandez, M. A. Location of mammograms ROI’s and reduction of false-positive. Comput. Methods Programs Biomed. 143, 97–111 (2017).

    Article  Google Scholar 

  10. Wingfield, C. et al. Relating dynamic brain states to dynamic machine states: human and machine solutions to the speech recognition problem. PLoS Comput. Biol. 13, e1005617 (2017).

    Article  Google Scholar 

  11. Yoshida, H. et al. Automated histological classification of whole-slide images of gastric biopsy specimens. Gastric Cancer 21, 249–257 (2018).

    Article  Google Scholar 

  12. Al-Khatib, S. M. et al. 2017 AHA/ACC/HRS guideline for management of patients with ventricular arrhythmias and the prevention of sudden cardiac death: executive summary. A report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Rhythm Society. Circulation 138, e210–e271 (2018).

    PubMed  Google Scholar 

  13. Yancy, C. W. et al. 2013 ACCF/AHA guideline for the management of heart failure: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. J. Am. Coll. Cardiol. 62, e147–e239 (2013).

    Article  Google Scholar 

  14. Heidenreich, P. A. et al. Forecasting the impact of heart failure in the United States: a policy statement from the American Heart Association. Circ. Heart Fail. 6, 606–619 (2013).

    Article  CAS  Google Scholar 

  15. Mozaffarian, D. et al. Heart disease and stroke statistics—2015 update: a report from the American Heart Association. Circulation 131, e29–e322 (2015).

    PubMed  Google Scholar 

  16. Bhalla, V. et al. Diagnostic ability of B-type natriuretic peptide and impedance cardiography: testing to identify left ventricular dysfunction in hypertensive patients. Am. J. Hypertens. 18, 73S–81S (2005).

    Article  CAS  Google Scholar 

  17. Costello-Boerrigter, L. C. et al. Amino-terminal pro-B-type natriuretic peptide and B-type natriuretic peptide in the general community: determinants and detection of left ventricular dysfunction. J. Am. Coll. Cardiol. 47, 345–353 (2006).

    Article  CAS  Google Scholar 

  18. Gruca, T. S., Pyo, T. H. & Nelson, G. C. Providing cardiology care in rural areas through visiting consultant clinics. J. Am. Heart Assoc. 5, e002909 (2016).

    Article  Google Scholar 

  19. Yancy, C. W. et al. 2017 ACC/AHA/HFSA focused update of the 2013 ACCF/AHA guideline for the management of heart failure: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Failure Society of America. J. Am. Coll. Cardiol. 70, 776–803 (2017).

    Article  Google Scholar 

  20. Yasin, O. Z. et al. Noninvasive blood potassium measurement using signal-processed, single-lead ECG acquired from a handheld smartphone. J. Electrocardiol. 50, 620–625 (2017).

    Article  Google Scholar 

  21. Yamani, H., Cai, Q. & Ahmad, M. Three-dimensional echocardiography in evaluation of left ventricular indices. Echocardiography 29, 66–75 (2012).

    Article  Google Scholar 

  22. Quinones, M. A. et al. A new, simplified and accurate method for determining ejection fraction with two-dimensional echocardiography. Circulation 64, 744–753 (1981).

    Article  CAS  Google Scholar 

  23. Russo, A. M. et al. ACCF/HRS/AHA/ASE/HFSA/SCAI/SCCT/SCMR 2013 appropriate use criteria for implantable cardioverter-defibrillators and cardiac resynchronization therapy: a report of the American College of Cardiology Foundation appropriate use criteria task force, Heart Rhythm Society, American Heart Association, American Society of Echocardiography, Heart Failure Society of America, Society for Cardiovascular Angiography and Interventions, Society of Cardiovascular Computed Tomography, and Society for Cardiovascular Magnetic Resonance. J. Am. Coll. Cardiol. 61, 1318–1368 (2013).

    Article  Google Scholar 

  24. van Rossum, G. Python Tutorial, Technical Report CS-R9526 (CWI, Amsterdam, 1995).

  25. Gholam-Hosseini, H. & Nazeran, H. Detection and extraction of the ECG signal parameters. In Proc. 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 127–130 (IEEE, 1998).

  26. Sugrue, A. et al. Identification of concealed and manifest long QT syndrome using a novel T wave analysis program. Circ. Arrhythm. Electrophysiol. 9, e003830 (2016).

    Article  Google Scholar 

  27. Couderc, J. P. et al. T-wave morphology abnormalities in benign, potent, and arrhythmogenic Ikr inhibition. Heart Rhythm 8, 1036–1043 (2011).

    Article  Google Scholar 

  28. Krizhevsky A., S I., Hinton G. E. ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems 1097–1105 (Neural Information Processing Systems, 2012).

  29. Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. Preprint at https://arxiv.org/abs/1412.6980 (2014).

  30. Ioffe, S. & Szegedy, C. Batch normalization: accelerating deep network training by reducing internal covariate shift. Preprint at https://arxiv.org/abs/1502.03167 (2015).

  31. Nagi, J. et al. Max-pooling convolutional neural networks for vision-based hand gesture recognition. In Proc. 2011 IEEE International Conference on Signal and Image Processing Applications 342–347 (IEEE, 2011).

  32. Wilson, F. N. et al. The precordial electrocardiogram. Am. Heart. J. 27, 19–85 (2004).

    Article  Google Scholar 

  33. Khan, G. M. A new electrode placement method for obtaining 12-lead ECGs. Open Heart 2, e000226 (2015).

    Article  Google Scholar 

  34. Cristianini, N. & Shawe-Taylor, J. An Introduction to Support Vector Machines and other Kernel-based Learning Methods (Cambridge University Press, New York, 2000).

Download references

Acknowledgements

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

Author information

Authors and Affiliations

Authors

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.

Corresponding author

Correspondence to Paul A. Friedman.

Ethics declarations

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.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Attia, Z.I., Kapa, S., Lopez-Jimenez, F. et al. Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram. Nat Med 25, 70–74 (2019). https://doi.org/10.1038/s41591-018-0240-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

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

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing