External validation of a deep learning electrocardiogram algorithm to detect ventricular dysfunction

Itzhak Zachi Attia, Andrew S. Tseng, Ernest Diez Benavente, Jose R. Medina-Inojosa, Taane G. Clark, Sofia Malyutina, Suraj Kapa, Henrik Schirmer, Alexander V. Kudryavtsev, Peter A. Noseworthy, Rickey E. Carter, Andrew Ryabikov, Pablo Perel, Paul A. Friedman, David A. Leon, Francisco Lopez-Jimenez

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Objective: To validate a novel artificial-intelligence electrocardiogram algorithm (AI-ECG) to detect left ventricular systolic dysfunction (LVSD) in an external population. Background: LVSD, even when asymptomatic, confers increased morbidity and mortality. We recently derived AI-ECG to detect LVSD using ECGs based on a large sample of patients treated at the Mayo Clinic. Methods: We performed an external validation study with subjects from the Know Your Heart Study, a cross-sectional study of adults aged 35–69 years residing in two cities in Russia, who had undergone both ECG and transthoracic echocardiography. LVSD was defined as left ventricular ejection fraction ≤ 35%. We assessed the performance of the AI-ECG to identify LVSD in this distinct patient population. Results: Among 4277 subjects in this external population-based validation study, 0.6% had LVSD (compared to 7.8% of the original clinical derivation study). The overall performance of the AI-ECG to detect LVSD was robust with an area under the receiver operating curve of 0.82. When using the LVSD probability cut-off of 0.256 from the original derivation study, the sensitivity, specificity, and accuracy in this population were 26.9%, 97.4%, 97.0%, respectively. Other probability cut-offs were analysed for different sensitivity values. Conclusions: The AI-ECG detected LVSD with robust test performance in a population that was very different from that used to develop the algorithm. Population-specific cut-offs may be necessary for clinical implementation. Differences in population characteristics, ECG and echocardiographic data quality may affect test performance.

Original languageEnglish (US)
Pages (from-to)130-135
Number of pages6
JournalInternational Journal of Cardiology
StatePublished - Apr 15 2021


  • Artificial intelligence
  • Electrocardiogram
  • Left ventricular systolic dysfunction
  • Machine learning

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine


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