Identification of hypertrophic cardiomyopathy on electrocardiographic images with deep learning
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Abstract
Hypertrophic cardiomyopathy (HCM) is frequently underdiagnosed. Although deep learning (DL) models using raw electrocardiographic (ECG) voltage data can enhance detection, their use at the point of care is limited. Here we report the development and validation of a DL model that detects HCM from images of 12-lead ECGs across layouts. The model was developed using 124,553 ECGs from 66,987 individuals at the Yale New Haven Hospital (YNHH), with HCM features determined by concurrent imaging (cardiac magnetic resonance (CMR) or echocardiography). External validation included ECG images from MIMIC-IV, the Amsterdam University Medical Center (AUMC) and the UK Biobank (UKB), where HCM was defined by CMR (YNHH, MIMIC-IV and AUMC) and diagnosis codes (UKB). The model demonstrated robust performance across image formats and sites (areas under the receiver operating characteristic curve (AUROCs): 0.95 internal testing; 0.94 MIMIC-IV; 0.92 AUMC; 0.91 UKB). Discriminative features localized to anterior/lateral leads (V4 and V5) regardless of layout. This approach enables scalable, image-based screening for HCM across clinical settings.
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Hypertrophic cardiomyopathy, cardiac amyloidosis, and pulmonary hypertension AI-ECG applications remain earlier or pathway-incomplete.
“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 [29]
The Discussion (page unavailable) says the HCM AI-ECG model needs prospective validation before widespread screening deployment and that a role for universal screening remains to be established. This supports describing the HCM application as pathway-incomplete. The paper does not address the development or implementation status of AI-ECG applications for cardiac amyloidosis or pulmonary hypertension. (LLM, conf=0.88)
Experimental — accuracy not yet measuredSangha et al. reported that a deep-learning model operating on ECG images detected HCM with AUROCs of 0.95 in the internal test set and 0.94, 0.92, and 0.91 across three external datasets.
“More recently, Sangha et al reported in a peer-reviewed study that a deep-learning model operating on ECG images—not raw waveform files—detected HCM with AUROCs of 0.95 in the internal test set and 0.94, 0.92, and 0.91 across three external datasets, supporting a potentially more deployable image-based screening route.29”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [29]
The Detection of HCM section reports an AUROC of 0.96 for the held-out internal test set, although it gives a range of 0.95–0.96 across ECG layouts. External Validation reports an AUROC of 0.94 for UK Biobank images; our copy does not report three external datasets with AUROCs of 0.94, 0.92, and 0.91. The paper does support the claim that the model detects HCM from ECG images. (LLM, conf=0.91)
Experimental — accuracy not yet measuredHCM and cardiac amyloidosis should be regarded as clinically important but pathway-incomplete domains rather than deployable screening indications comparable to low-LVEF AI-ECG.
“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 [29]
The Discussion (page unavailable) says prospective validation is needed before widespread HCM screening deployment and that the role of universal screening remains to be established. This supports the HCM portion of the claim. The paper does not address cardiac amyloidosis or compare HCM screening with low-LVEF AI-ECG, so it does not support the full sentence attributed to the citation group. (LLM, conf=0.86)
Experimental — accuracy not yet measured
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Dhingra, L. S., Croon, P. M., Oikonomou, E. K., & Khera, R. (2026) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztag057 · Cited by 2 · see citing works
Self-citationSwain, W. H., Giudicessi, J. R., Geske, J. B., Newman, D. B., Bos, J. M., Karim, S., Noseworthy, P. A., Attia, Z. I., Friedman, P. A., Arruda-Olson, A. M., et al. (2025) · Heart Rhythm O2 · doi:10.1016/j.hroo.2025.08.040 · Cited by 1 · see citing works
Dhingra, L. S., Croon, P. M., Batinica, B., Aminorroaya, A., Pedroso, A. F., & Khera, R. (2026) · Current Heart Failure Reports · doi:10.1007/s11897-026-00748-x · Cited by 2 · see citing works
Self-citationChoi, R. B., & Khera, R. (2026) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztag091 · Cited by 1 · see citing works
Self-citationAn AlexNet Model for Diagnosing Left Ventricular Hypertrophy Using ECG Signals
Ryakala, D., & Srikaanth, P. B. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-97-8946-7_17 · Cited by 1 · see citing works
Detection of hypertrophic cardiomyopathy on electrocardiogram using artificial intelligencePreprint
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Electrocardiogram-Based Deep Learning to Prioritize Testing for Transthyretin Amyloid Cardiomyopathy
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CNN-Based Approach for the Classification of Hypertrophic Cardiomyopathy
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- Authors
- Sangha, V., Dhingra, L. S., Aminorroaya, A., Croon, P. M., Sikand, N. V., Sen, S., Martinez, M. W., Maron, M. S., Krumholz, H. M., Asselbergs, F. W., Oikonomou, E. K., & Khera, R.
- Affiliations
- Yale University (US)
- Amsterdam University Medical Centers (NL)
- Morristown Medical Center (US)
- Tufts Medical Center (US)
- Yale New Haven Hospital (US)
- National Institute for Health and Care Research (GB)
- Corresponding author
- Khera, Rohan
- ORCID
- Venue
- Nature Cardiovascular Research
- Published
- 2025-07-22
- Volume
- 4
- Issue
- 8
- Pages
- 991-1000
- ISSN
- 2731-0590 (ISSN-L)
- PMC
- PMC10793540
- OpenAlex
- W4412552358
- Cited by
- 41
- References
- 48
- Citations by year
- 2026: 16 · 2025: 14 · 2024: 11
- FWCI
- 20.96
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Cardiovascular Research (publishedVersion)
- medRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion, cc-by-nc-nd)
- UCL Discovery (University College London) (submittedVersion)
- Pure Amsterdam UMC (submittedVersion)
- Funders
- U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (K23HL153775, R01HL167858, 1F32HL170592-01)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiomyopathy and Myosin Studies
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac electrophysiology and arrhythmias
- Keywords
- hypertrophic cardiomyopathy, electrocardiogram, 12-lead ECG, deep learning, automated disease detection, cardiac magnetic resonance, echocardiography, external validation, MIMIC-IV, UK Biobank
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