TY - JOUR
T1 - Detection of hypertrophic cardiomyopathy by an artificial intelligence electrocardiogram in children and adolescents
AU - Siontis, Konstantinos C.
AU - Liu, Kan
AU - Bos, J. Martijn
AU - Attia, Zachi I.
AU - Cohen-Shelly, Michal
AU - Arruda-Olson, Adelaide M.
AU - Zanjirani Farahani, Nasibeh
AU - Friedman, Paul A.
AU - Noseworthy, Peter A.
AU - Ackerman, Michael J.
N1 - Funding Information:
This work was supported by the Paul and Ruby Tsai and Family Hypertrophic Cardiomyopathy Research Fund, The Louis V. Gerstner Jr. Fund at Vanguard Charitable, and the Mayo Clinic Windland Smith Rice Comprehensive Sudden Cardiac Death Program.
Publisher Copyright:
© 2021
PY - 2021/10/1
Y1 - 2021/10/1
N2 - Background: There is no established screening approach for hypertrophic cardiomyopathy (HCM). We recently developed an artificial intelligence (AI) model for the detection of HCM based on the 12‑lead electrocardiogram (AI-ECG) in adults. Here, we aimed to validate this approach of ECG-based HCM detection in pediatric patients (age ≤ 18 years). Methods: We identified a cohort of 300 children and adolescents with HCM (mean age 12.5 ± 4.6 years, male 68%) who had an ECG and echocardiogram at our institution. Patients were age- and sex-matched to 18,439 non-HCM controls. Diagnostic performance of the AI-ECG model for the detection of HCM was estimated using the previously identified optimal diagnostic threshold of 11% (the probability output derived by the model above which an ECG is considered to belong to an HCM patient). Results: Mean AI-ECG probabilities of HCM were 92% and 5% in the case and control groups, respectively. The area under the receiver operating characteristic curve (AUC) of the AI-ECG model for HCM detection was 0.98 (95% CI 0.98–0.99) with corresponding sensitivity 92% and specificity 95%. The positive and negative predictive values were 22% and 99%, respectively. The model performed similarly in males and females and in genotype-positive and genotype-negative HCM patients. Performance tended to be superior with increasing age. In the age subgroup <5 years, the test's AUC was 0.93. In comparison, the AUC was 0.99 in the age subgroup 15–18 years. Conclusions: A deep-learning, AI model can detect pediatric HCM with high accuracy from the standard 12‑lead ECG.
AB - Background: There is no established screening approach for hypertrophic cardiomyopathy (HCM). We recently developed an artificial intelligence (AI) model for the detection of HCM based on the 12‑lead electrocardiogram (AI-ECG) in adults. Here, we aimed to validate this approach of ECG-based HCM detection in pediatric patients (age ≤ 18 years). Methods: We identified a cohort of 300 children and adolescents with HCM (mean age 12.5 ± 4.6 years, male 68%) who had an ECG and echocardiogram at our institution. Patients were age- and sex-matched to 18,439 non-HCM controls. Diagnostic performance of the AI-ECG model for the detection of HCM was estimated using the previously identified optimal diagnostic threshold of 11% (the probability output derived by the model above which an ECG is considered to belong to an HCM patient). Results: Mean AI-ECG probabilities of HCM were 92% and 5% in the case and control groups, respectively. The area under the receiver operating characteristic curve (AUC) of the AI-ECG model for HCM detection was 0.98 (95% CI 0.98–0.99) with corresponding sensitivity 92% and specificity 95%. The positive and negative predictive values were 22% and 99%, respectively. The model performed similarly in males and females and in genotype-positive and genotype-negative HCM patients. Performance tended to be superior with increasing age. In the age subgroup <5 years, the test's AUC was 0.93. In comparison, the AUC was 0.99 in the age subgroup 15–18 years. Conclusions: A deep-learning, AI model can detect pediatric HCM with high accuracy from the standard 12‑lead ECG.
KW - Artificial intelligence
KW - Deep learning
KW - Electrocardiogram
KW - Hypertrophic cardiomyopathy
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U2 - 10.1016/j.ijcard.2021.08.026
DO - 10.1016/j.ijcard.2021.08.026
M3 - Article
C2 - 34419527
AN - SCOPUS:85113392886
VL - 340
SP - 42
EP - 47
JO - International Journal of Cardiology
JF - International Journal of Cardiology
SN - 0167-5273
ER -