Detection of Hypertrophic Cardiomyopathy Using a Convolutional Neural Network-Enabled Electrocardiogram

Wei Yin Ko, Konstantinos C. Siontis, Zachi I. Attia, Rickey E. Carter, Suraj Kapa, Steve R. Ommen, Steven J. Demuth, Michael J. Ackerman, Bernard J. Gersh, Adelaide M. Arruda-Olson, Jeffrey B. Geske, Samuel J. Asirvatham, Francisco Lopez-Jimenez, Rick A. Nishimura, Paul A. Friedman, Peter A. Noseworthy

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

Background: Hypertrophic cardiomyopathy (HCM) is an uncommon but important cause of sudden cardiac death. Objectives: This study sought to develop an artificial intelligence approach for the detection of HCM based on 12-lead electrocardiography (ECG). Methods: A convolutional neural network (CNN) was trained and validated using digital 12-lead ECG from 2,448 patients with a verified HCM diagnosis and 51,153 non-HCM age- and sex-matched control subjects. The ability of the CNN to detect HCM was then tested on a different dataset of 612 HCM and 12,788 control subjects. Results: In the combined datasets, mean age was 54.8 ± 15.9 years for the HCM group and 57.5 ± 15.5 years for the control group. After training and validation, the area under the curve (AUC) of the CNN in the validation dataset was 0.95 (95% confidence interval [CI]: 0.94 to 0.97) at the optimal probability threshold of 11% for having HCM. When applying this probability threshold to the testing dataset, the CNN's AUC was 0.96 (95% CI: 0.95 to 0.96) with sensitivity 87% and specificity 90%. In subgroup analyses, the AUC was 0.95 (95% CI: 0.94 to 0.97) among patients with left ventricular hypertrophy by ECG criteria and 0.95 (95% CI: 0.90 to 1.00) among patients with a normal ECG. The model performed particularly well in younger patients (sensitivity 95%, specificity 92%). In patients with HCM with and without sarcomeric mutations, the model-derived median probabilities for having HCM were 97% and 96%, respectively. Conclusions: ECG-based detection of HCM by an artificial intelligence algorithm can be achieved with high diagnostic performance, particularly in younger patients. This model requires further refinement and external validation, but it may hold promise for HCM screening.

Original languageEnglish (US)
Pages (from-to)722-733
Number of pages12
JournalJournal of the American College of Cardiology
Volume75
Issue number7
DOIs
StatePublished - Feb 25 2020

Keywords

  • artificial intelligence
  • diagnostic performance
  • electrocardiogram
  • hypertrophic cardiomyopathy

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine

Fingerprint Dive into the research topics of 'Detection of Hypertrophic Cardiomyopathy Using a Convolutional Neural Network-Enabled Electrocardiogram'. Together they form a unique fingerprint.

  • Cite this