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Detection of Hypertrophic Cardiomyopathy Using a Convolutional Neural Network-Enabled Electrocardiogram
Open accessAbstract
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.
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In the citing paper
“hypertrophic cardiomyopathy, cardiac amyloidosis, and pulmonary hypertension remain earlier or pathway-incomplete applications.”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [26]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Taken together, HCM and CA should therefore be regarded as clinically important but pathway-incomplete domains rather than deployable screening indications comparable to low-LVEF AI-ECG.26–30,43”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [26]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“HCM has shown consistently high discrimination, international external validation, and early real-world implementation, making it one of the more reproducible structural AI-ECG targets.”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [10]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The application of deep learning, a subset of artificial intelligence (AI), has been shown to be beneficial in the detection of specific heart diseases from the 12-lead electrocardiogram (ECG). This includes conditions such as aortic stenosis, low left ventricular ejection fraction (LVEF) and low left ventricular hypertrophy, as well as composites of many valvular diseases3,17–21.”
From Detecting structural heart disease from electrocardiograms using AI (2025) · cites this paper as [20]
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“Ko et al at the Mayo Clinic developed the first AI-ECG model for HCM detection, achieving an AUC of 0.96 in an independent testing cohort, with 87% sensitivity and 90% specificity at the prespecified threshold.”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [26]
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“In a landmark study, Ko et al. developed a convolutional neural network using standard 12-lead ECGs to detect HCM and reported strong diagnostic performance, with an AUC of 0.96, sensitivity of 87%, and specificity of 90% [10].”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [10]
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“While these models provide a strong foundation for the role of ECG-based detection of SHDs, the low prevalence of these individual diseases, their potential implementation for broad screening is limited by the low PPVs of the proposed models.10-12,16,32-34”
From Ensemble Deep Learning Algorithm for Structural Heart Disease Screening Using Electrocardiographic Images (2025) · cites this paper as [32]
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“Prior studies have reported the use of deep learning on 12-lead ECGs to detect individual structural cardiovascular conditions, including LVSD,10,11,13 hypertrophic cardiomyopathy,16,32,33 cardiac amyloidosis,34,35 aortic stenosis,12 among others.15,35-37”
From Ensemble Deep Learning Algorithm for Structural Heart Disease Screening Using Electrocardiographic Images (2025) · cites this paper as [32]
In the citing paper
“The study found that the algorithm generally performed better in the adolescent group than in the pediatric group.”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [154]
In the citing paper
“The algorithm performed impressively in adult HCM patient ECG detection, with an AUC of 0.96, sensitivity of 87%, and specificity of 90%.”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [154]
In the citing paper
“The algorithm’s performance in a test of 300 children and over 18,000 age and gender-matched controls was equally impressive: the HCM detection model achieved an AUC of 0.98, sensitivity of 92%, specificity of 95%, Positive Predictive Value (PPV) of 22%, and Negative Predictive Value (NPV) of 99%.”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [154]
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Citations
Cites
Prevalence of Hypertrophic Cardiomyopathy in a General Population of Young Adults
Maron, B. J., Gardin, J. M., Flack, J. M., Gidding, S. S., Kurosaki, T., & Bild, D. E. (1995) · Circulation · doi:10.1161/01.cir.92.4.785 · Cited by 2,274
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Self-citation2011 ACCF/AHA Guideline for the Diagnosis and Treatment of Hypertrophic Cardiomyopathy
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Recommendations for interpretation of 12-lead electrocardiogram in the athlete
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Mitigating bias in clinical machine learning models
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Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease
Shaulian, S., Zeltser, R., & Makaryus, A. N. (2026) · Diagnostics · doi:10.3390/diagnostics16172834
Metadata
- Authors
- Ko, W.-Y., Siontis, K. C., Attia, Z. I., Carter, R. E., Kapa, S., Ommen, S. R., Demuth, S. J., Ackerman, M. J., Gersh, B. J., Arruda-Olson, A. M., Geske, J. B., Asirvatham, S. J., Lopez-Jimenez, F., Nishimura, R. A., Friedman, P. A., & Noseworthy, P. A.
- Affiliations
- Mayo Clinic (US)
- Mayo Clinic in Florida (US)
- Corresponding author
- Noseworthy, Peter A.
- ORCID
- Venue
- Journal of the American College of Cardiology
- Publisher
- Elsevier BV
- Published
- 2020-02
- Volume
- 75
- Issue
- 7
- Pages
- 722-733
- ISSN
- 0735-1097 (ISSN-L)
- OpenAlex
- W3008117014
- Cited by
- 404
- References
- 30
- Citations by year
- 2026: 53 · 2025: 89 · 2024: 86 · 2023: 70 · 2022: 41 · 2021: 47 · 2020: 17
- FWCI
- 27.07
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of the American College of Cardiology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Heart, Lung, and Blood Institute (K01HL124045)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac electrophysiology and arrhythmias
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- hypertrophic cardiomyopathy, electrocardiogram, convolutional neural networks, artificial intelligence, automated disease detection, left ventricular hypertrophy, sarcomeric mutations, sudden cardiac death, diagnostic accuracy
- MeSH terms
- Adult, Aged, Algorithms, Cardiomyopathy, Hypertrophic, Electrocardiography, Female, Humans, Male, Middle Aged, Models, Cardiovascular, Neural Networks, Computer
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