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

Hypertrophic cardiomyopathy (HCM) is an uncommon but important cause of sudden cardiac death. This study sought to develop an artificial intelligence approach for the detection of HCM based on 12-lead electrocardiography (ECG). A convolutional neural network (CNN) was trained and validated using dig...

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Published in:	Journal of the American College of Cardiology Vol. 75; no. 7; p. 722
Main Authors:	Ko, Wei-Yin, Siontis, Konstantinos C, Attia, Zachi I, Carter, Rickey E, Kapa, Suraj, Ommen, Steve R, Demuth, Steven J, Ackerman, Michael J, Gersh, Bernard J, Arruda-Olson, Adelaide M, Geske, Jeffrey B, Asirvatham, Samuel J, Lopez-Jimenez, Francisco, Nishimura, Rick A, Friedman, Paul A, Noseworthy, Peter A
Format:	Journal Article
Language:	English
Published:	United States 25.02.2020
Subjects:	electrocardiogram diagnostic performance hypertrophic cardiomyopathy artificial intelligence
ISSN:	1558-3597, 1558-3597
Online Access:	Get more information
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Abstract	Hypertrophic cardiomyopathy (HCM) is an uncommon but important cause of sudden cardiac death. This study sought to develop an artificial intelligence approach for the detection of HCM based on 12-lead electrocardiography (ECG). 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. 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. 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.
AbstractList	Hypertrophic cardiomyopathy (HCM) is an uncommon but important cause of sudden cardiac death. This study sought to develop an artificial intelligence approach for the detection of HCM based on 12-lead electrocardiography (ECG). 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. 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. 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. Hypertrophic cardiomyopathy (HCM) is an uncommon but important cause of sudden cardiac death.BACKGROUNDHypertrophic cardiomyopathy (HCM) is an uncommon but important cause of sudden cardiac death.This study sought to develop an artificial intelligence approach for the detection of HCM based on 12-lead electrocardiography (ECG).OBJECTIVESThis study sought to develop an artificial intelligence approach for the detection of HCM based on 12-lead electrocardiography (ECG).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.METHODSA 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.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.RESULTSIn 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.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.CONCLUSIONSECG-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.
Author	Arruda-Olson, Adelaide M Ko, Wei-Yin Demuth, Steven J Attia, Zachi I Nishimura, Rick A Asirvatham, Samuel J Gersh, Bernard J Kapa, Suraj Siontis, Konstantinos C Ommen, Steve R Ackerman, Michael J Lopez-Jimenez, Francisco Friedman, Paul A Geske, Jeffrey B Noseworthy, Peter A Carter, Rickey E
Author_xml	– sequence: 1 givenname: Wei-Yin surname: Ko fullname: Ko, Wei-Yin organization: Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota – sequence: 2 givenname: Konstantinos C surname: Siontis fullname: Siontis, Konstantinos C organization: Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota – sequence: 3 givenname: Zachi I surname: Attia fullname: Attia, Zachi I organization: Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota – sequence: 4 givenname: Rickey E surname: Carter fullname: Carter, Rickey E organization: Health Sciences Research, Mayo Clinic College of Medicine, Jacksonville, Florida – sequence: 5 givenname: Suraj surname: Kapa fullname: Kapa, Suraj organization: Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota – sequence: 6 givenname: Steve R surname: Ommen fullname: Ommen, Steve R organization: Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota – sequence: 7 givenname: Steven J surname: Demuth fullname: Demuth, Steven J organization: Information Technology, Mayo Clinic, Rochester, Minnesota – sequence: 8 givenname: Michael J surname: Ackerman fullname: Ackerman, Michael J organization: Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota – sequence: 9 givenname: Bernard J surname: Gersh fullname: Gersh, Bernard J organization: Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota – sequence: 10 givenname: Adelaide M surname: Arruda-Olson fullname: Arruda-Olson, Adelaide M organization: Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota – sequence: 11 givenname: Jeffrey B surname: Geske fullname: Geske, Jeffrey B organization: Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota – sequence: 12 givenname: Samuel J surname: Asirvatham fullname: Asirvatham, Samuel J organization: Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota – sequence: 13 givenname: Francisco surname: Lopez-Jimenez fullname: Lopez-Jimenez, Francisco organization: Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota – sequence: 14 givenname: Rick A surname: Nishimura fullname: Nishimura, Rick A organization: Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota – sequence: 15 givenname: Paul A surname: Friedman fullname: Friedman, Paul A organization: Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota – sequence: 16 givenname: Peter A surname: Noseworthy fullname: Noseworthy, Peter A email: Noseworthy.Peter@mayo.edu organization: Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota. Electronic address: Noseworthy.Peter@mayo.edu
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/32081280$$D View this record in MEDLINE/PubMed
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Copyright	Copyright © 2020 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.
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Keywords	electrocardiogram diagnostic performance hypertrophic cardiomyopathy artificial intelligence
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Snippet	Hypertrophic cardiomyopathy (HCM) is an uncommon but important cause of sudden cardiac death. This study sought to develop an artificial intelligence approach... Hypertrophic cardiomyopathy (HCM) is an uncommon but important cause of sudden cardiac death.BACKGROUNDHypertrophic cardiomyopathy (HCM) is an uncommon but...
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Title	Detection of Hypertrophic Cardiomyopathy Using a Convolutional Neural Network-Enabled Electrocardiogram
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