Artificial Intelligence-Enabled ECG Algorithm to Identify Patients With Left Ventricular Systolic Dysfunction Presenting to the Emergency Department with Dyspnea
Abstract
Background Identification of systolic heart failure among patients presenting to the emergency department (ED) with acute dyspnea is challenging. The reasons for dyspnea are often multifactorial. A focused physical evaluation and diagnostic testing can lack sensitivity and specificity. The objective of this study was to assess the accuracy of an artificial intelligence-enabled ECG to identify patients presenting with dyspnea who have left ventricular systolic dysfunction (LVSD). Methods We retrospectively applied a validated artificial intelligence-enabled ECG algorithm for the identification of LVSD (defined as LV ejection fraction ≤35%) to a cohort of patients aged ≥18 years who were evaluated in the ED at a Mayo Clinic site with dyspnea. Patients were included if they had at least one standard 12-lead ECG acquired on the date of the ED visit and an echocardiogram performed within 30 days of presentation. Patients with prior LVSD were excluded. We assessed the model performance using area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity. Results A total of 1606 patients were included. Median time from ECG to echocardiogram was 1 day (Q1: 1, Q3: 2). The artificial intelligence-enabled ECG algorithm identified LVSD with an area under the receiver operating characteristic curve of 0.89 (95% CI, 0.86-0.91) and accuracy of 85.9%. Sensitivity, specificity, negative predictive value, and positive predictive value were 74%, 87%, 97%, and 40%…
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AI-ECG has a dual role: opportunistic screening in asymptomatic patients and evaluation of symptomatic patients in whom ventricular dysfunction is part of the differential.
“Read together, these studies point to a dual role: opportunistic screening in asymptomatic patients, and evaluation of symptomatic patients in whom ventricular dysfunction is part of the differential.”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [18]
AI-ECG may assist in triage settings where rapid identification of cardiac dysfunction can guide additional testing.
“Adedinsewo et al. evaluated an AI-enabled ECG algorithm in emergency department patients presenting with dyspnea, showing that AI-ECG may also assist in triage settings where rapid identification of cardiac dysfunction can guide additional testing [18].”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [18]
Deep learning has been shown to be beneficial in detecting specific heart diseases from the 12-lead ECG, including aortic stenosis, low LVEF, low left ventricular hypertrophy and composites of valvular diseases.
“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 [19]
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References 44 · Cited by 169
- Authors
- Adedinsewo, D., Carter, R. E., Attia, Z., Johnson, P., Kashou, A. H., Dugan, J. L., Albus, M., Sheele, J. M., Bellolio, F., Friedman, P. A., Lopez-Jimenez, F., & Noseworthy, P. A.
- Affiliations
- Mayo Clinic (US)
- Mayo Clinic in Florida (US)
- Division of Cardiovascular Medicine (Z.A., J.L.D., P.A.F., F.L.-J., P.A.N.), Mayo Clinic, Rochester, MN.
- ORCID
- Venue
- Circulation: Arrhythmia and Electrophysiology
- Published
- 2020-08
- Volume
- 13
- Issue
- 8
- Pages
- e008437-e008437
- ISSN
- 1941-3084 (ISSN-L)
- 1941-3149
- OpenAlex
- W3047391569
- Cited by
- 169
- References
- 44
- Citations by year
- 2026: 28 · 2025: 29 · 2024: 31 · 2023: 17 · 2022: 35 · 2021: 27 · 2020: 1
- FWCI
- 12.38
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Circulation Arrhythmia and Electrophysiology (publishedVersion)
- PubMed (publishedVersion)
- 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, acute dyspnea, emergency department, heart failure with reduced ejection fraction, NT-proBNP, ejection fraction, diagnostic accuracy, 12-lead ECG, echocardiography, point-of-care diagnosis, risk stratification, mortality risk
- MeSH terms
- Aged, Artificial Intelligence, Diagnosis, Computer-Assisted, Dyspnea, Electrocardiography, Emergency Medical Services, Female, Humans, Male, Middle Aged, Predictive Value of Tests, Retrospective Studies, Signal Processing, Computer-Assisted, Stroke Volume, Systole, Reproducibility of Results, Ventricular Function, Left, Cardiology Service, Hospital, Ventricular Dysfunction, Left, Heart Failure, Systolic
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