Detection of Left Ventricular Systolic Dysfunction From Electrocardiographic Images
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Abstract
BACKGROUND: Left ventricular (LV) systolic dysfunction is associated with a >8-fold increased risk of heart failure and a 2-fold risk of premature death. The use of ECG signals in screening for LV systolic dysfunction is limited by their availability to clinicians. We developed a novel deep learning–based approach that can use ECG images for the screening of LV systolic dysfunction. METHODS: Using 12-lead ECGs plotted in multiple different formats, and corresponding echocardiographic data recorded within 15 days from the Yale New Haven Hospital between 2015 and 2021, we developed a convolutional neural network algorithm to detect an LV ejection fraction <40%. The model was validated within clinical settings at Yale New Haven Hospital and externally on ECG images from Cedars Sinai Medical Center in Los Angeles, CA; Lake Regional Hospital in Osage Beach, MO; Memorial Hermann Southeast Hospital in Houston, TX; and Methodist Cardiology Clinic of San Antonio, TX. In addition, it was validated in the prospective Brazilian Longitudinal Study of Adult Health. Gradient-weighted class activation mapping was used to localize class-discriminating signals on ECG images. RESULTS: Overall, 385 601 ECGs with paired echocardiograms were used for model development. The model demonstrated high discrimination across various ECG image formats and calibrations in internal validation (area under receiving operation characteristics [AUROCs], 0.91; area under precision-recall curve [AUPRC], 0.55); and external sets of ECG images from Cedars Sinai (AUROC, 0.90 and AUPRC, 0.53), outpatient Yale New Haven Hospital clinics (AUROC, 0.94 and AUPRC, 0.77), Lake Regional Hospital (AUROC, 0.90 and AUPRC, 0.88), Memorial Hermann Southeast Hospital (AUROC, 0.91 and AUPRC 0.88), Methodist Cardiology Clinic (AUROC, 0.90 and AUPRC, 0.74), and Brazilian Longitudinal Study of Adult Health cohort (AUROC, 0.95 and AUPRC, 0.45). An ECG suggestive of LV systolic dysfunction portended >27-fold higher odds of LV systolic dysfunction on transthoracic echocardiogram (odds ratio, 27.5 [95% CI, 22.3–33.9] in the held-out set). Class-discriminative patterns localized to the anterior and anteroseptal leads (V 2 and V 3 ), corresponding to the left ventricle regardless of the ECG layout. A positive ECG screen in individuals with an LV ejection fraction ≥40% at the time of initial assessment was associated with a 3.9-fold increased risk of developing incident LV systolic dysfunction in the future (hazard ratio, 3.9 [95% CI, 3.3–4.7]; median follow-up, 3.2 years). CONCLUSIONS: We developed and externally validated a deep learning model that identifies LV systolic dysfunction from ECG images. This approach represents an automated and accessible screening strategy for LV systolic dysfunction, particularly in low-resource settings.
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A key challenge of AI-ECG models in detecting specific cardiac disorders using ECGs is low precision driven by the low prevalence of individual disorders.
“A key challenge of AI-ECG models in detecting specific cardiac disorders using ECGs is the low precision driven by the low prevalence of individual disorders.10-12”
From Ensemble Deep Learning Algorithm for Structural Heart Disease Screening Using Electrocardiographic Images (2025) · cites this paper as [11]
The Results (page unavailable) report a positive predictive value of 0.26 for the AI-ECG model detecting left ventricular systolic dysfunction. The Discussion (page unavailable) notes lower disorder prevalence in a healthier validation cohort, but does not say that low prevalence drove low precision or establish this as a general challenge across individual cardiac disorders. (LLM, conf=0.82)
Experimental — accuracy not yet measuredThe low prevalence of individual structural heart diseases limits the potential implementation of models for broad screening because of the low PPVs of the proposed models.
“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 [11]
The Results (Detection of LV Systolic Dysfunction; page unavailable) report a PPV of 0.26. The Discussion (page unavailable) notes lower disease prevalence in a healthier validation cohort and says screening thresholds may need recalibration. The paper does not conclude that low prevalence and PPV limit broad screening implementation; it calls for prospective assessment in that setting. (LLM, conf=0.84)
Experimental — accuracy not yet measuredPreviously, applications of artificial intelligence for electrocardiograms have shown potential to detect signatures of structural heart diseases.
“Previously applications of artificial intelligence for electrocardiograms (AI-ECG) have shown potential to detect signatures of SHDs.10-18”
From Ensemble Deep Learning Algorithm for Structural Heart Disease Screening Using Electrocardiographic Images (2025) · cites this paper as [11]
Prior studies have reported the use of deep learning on 12-lead ECGs to detect individual structural cardiovascular conditions, including LVSD, hypertrophic cardiomyopathy, cardiac amyloidosis, and aortic stenosis.
“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 [11]
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Self-citationAHA/ACC/ESC/WHF Expert Consensus Document: Second Universal Definition of Heart Failure (2026)
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Teaching multimodal LLMs to comprehend 12-lead electrocardiographic images
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Self-citationUsing artificial intelligence to spot heart failure from ECGs: is it prime time?
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Artificial Intelligence Electrocardiogram and Left Ventricular Systolic Dysfunction in Kenya
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Diagnostic and Prognostic Electrocardiogram-Based Models for Rapid Clinical Applications
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Artificial Intelligence-Enabled Electrocardiography in Practice: A State-of-the-Art Review
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- Authors
- Sangha, V., Nargesi, A. A., Dhingra, L. S., Khunte, A., Mortazavi, B. J., Ribeiro, A. H., Banina, E., Adeola, O., Garg, N., Brandt, C. A., Miller, E. J., Ribeiro, A. L. P., Velazquez, E. J., Giatti, L., Barreto, S. M., Foppa, M., Yuan, N., Ouyang, D., Krumholz, H. M., & Khera, R.
- Affiliations
- Yale University (US)
- Brigham and Women's Hospital (US)
- Yale New Haven Hospital (US)
- Universidade Federal de Minas Gerais (BR)
- Lake Regional Hospital (US)
- Methodist Healthcare (US)
- Memorial Hermann (US)
- Telehealth Center and Cardiology Service, Hospital das Clínicas (A.L.P.R.), Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.
- Universidade Federal do Rio Grande do Sul (BR)
- San Francisco VA Medical Center (US)
- Cedars-Sinai Medical Center (US)
- Corresponding author
- Khera, Rohan
- ORCID
- Sangha, Veer
- Nargesi, Arash A.
- Dhingra, Lovedeep S.
- Khunte, Akshay
- Mortazavi, Bobak J.
- Ribeiro, Antônio H.
- Banina, Evgeniya
- Adeola, Oluwaseun
- Garg, Nadish
- Brandt, Cynthia A.
- Miller, Edward J.
- Ribeiro, Antonio Luiz P.
- Velazquez, Eric J.
- Giatti, Luana
- Barreto, Sandhi M.
- Foppa, Murilo
- Yuan, Neal
- Ouyang, David
- Krumholz, Harlan M.
- Khera, Rohan
- Venue
- Circulation
- Published
- 2023-08-29
- Volume
- 148
- Issue
- 9
- Pages
- 765-777
- ISSN
- 0009-7322 (ISSN-L)
- 1524-4539
- PMC
- PMC10982757
- OpenAlex
- W4385230416
- Cited by
- 150
- References
- 26
- Citations by year
- 2026: 48 · 2025: 53 · 2024: 37 · 2023: 12
- FWCI
- 29.52
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc
- Locations
- Circulation (publishedVersion)
- medRxiv (acceptedVersion, cc-by-nc)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- U.S. Department of Defense
- Bristol-Myers Squibb
- Yale University
- Shenzhen Center for Health Information
- Conselho Nacional de Desenvolvimento Científico e Tecnológico
- Novo Nordisk
- Fundação de Amparo à Pesquisa do Estado de Minas Gerais
- National Institutes of Health
- Advanced Research Projects Agency
- National Heart, Lung, and Blood Institute (K23HL153775)
- National Institute of Biomedical Imaging and Bioengineering
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- left ventricular systolic dysfunction, ECG images, 12-lead ECG, deep learning, convolutional neural networks, left ventricular ejection fraction, heart failure screening, automated screening, Grad-CAM, resource-limited settings
- MeSH terms
- Adult, Electrocardiography, Humans, Longitudinal Studies, Prospective Studies, Ventricular Function, Left, Ventricular Dysfunction, Left
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