ArticleInternational journal of cardiology2021
Left ventricular systolic dysfunction identification using artificial intelligence-augmented electrocardiogram in cardiac intensive care unit patients.
Article in International journal of cardiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it, 51 citations in OpenAlex.
- Artificial intelligence enabled ECG screening for left ventricular systolic dysfunction: a systematic review.Heart failure reviews · 2023Pooled it
- External validation of artificial intelligence-guided electrocardiogram (AI-ECG) in detecting asymptomatic left ventricular dysfunction: A systematic review and meta-analysis.American journal of preventive cardiology · 2027Article
- New Insights into Cardiac Intensive Care.Reviews in cardiovascular medicine · 2026Review
- Heart failure detection in electrocardiograms using artificial intelligence and pragmatic labelling.NPJ digital medicine · 2026Article
- Self-supervised contrastive learning enables robust electrocardiogram-based cardiac classification.Heart rhythm O2 · 2026Article
- Hierarchical fusion of electrocardiogram and phonocardiogram data improves heart failure detection.Patterns (New York, N.Y.) · 2026Article
- Deep learning-enabled ECG system for detecting left ventricular hypertrophy and predicting cardiovascular prognoses.BioData mining · 2026Article
- A Screening Method for Determining Left Ventricular Systolic Function Based on Spectral Analysis of a Single-Channel Electrocardiogram Using Machine Learning Algorithms.Diagnostics (Basel, Switzerland) · 2026Article
- Enhanced prediction of left ventricular ejection fraction using electrocardiography with the addition of clinical metadata.The Korean journal of internal medicine · 2026Article
- Artificial intelligence in ECG-based diagnosis of low left ventricular ejection fraction: a systematic review and meta-analysis.Biomedical engineering letters · 2025Review
- Hearts, Data, and Artificial Intelligence Wizardry: From Imitation to Innovation in Cardiovascular Care.Biomedicines · 2025Review
- Artificial Intelligence ECG Diastolic Dysfunction and Survival in Cardiac Intensive Care Unit Patients.Journal of the American Heart Association · 2025Article
- Key Concepts in Machine Learning and Clinical Applications in the Cardiac Intensive Care Unit.Current cardiology reports · 2025Review
- Detection of Right and Left Ventricular Dysfunction in Pediatric Patients Using Artificial Intelligence-Enabled ECGs.Journal of the American Heart Association · 2024Article
- Machine Learning Prediction of Blood Potassium at Different Time Cutoffs.Computing in cardiology · 2024Article
- Performance of off-the-shelf machine learning architectures and biases in low left ventricular ejection fraction detection.Heart rhythm O2 · 2024Article
- Myocardial scar and left ventricular ejection fraction classification for electrocardiography image using multi-task deep learning.Scientific reports · 2024Article
- Artificial intelligence-based identification of left ventricular systolic dysfunction from 12-lead electrocardiograms: external validation and advanced application of an existing model.European heart journal. Digital health · 2024Article
- Comparison of Machine Learning Detection of Low Left Ventricular Ejection Fraction Using Individual ECG Leads.Computing in cardiology · 2023Article
- Electrocardiogram-based artificial intelligence for the diagnosis of heart failure: a systematic review and meta-analysis.Journal of geriatric cardiology : JGC · 2022Article
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7 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundAn artificial intelligence-augmented electrocardiogram (AI-ECG) can identify left ventricular systolic dysfunction (LVSD). We examined the accuracy of AI ECG for identification of LVSD (defined as LVEF ≤40% by transthoracic echocardiogram [TTE]) in cardiac intensive care unit (CICU) patients.
methodWe included unique Mayo Clinic CICU patients admitted from 2007 to 2018 who underwent AI-ECG and TTE within 7 days, at least one of which was during hospitalization. Discrimination of the AI-ECG for LVSD was determined using receiver-operator characteristic curve (AUC) values.
resultsWe included 5680 patients with a mean age of 68 ± 15 years (37% females). Acute coronary syndrome (ACS) was present in 55%. LVSD was present in 34% of patients (mean LVEF 48 ± 16%). The AI-ECG had an AUC of 0.83 (95% confidence interval 0.82-0.84) for discrimination of LVSD. Using the optimal cut-off, the AI-ECG had 73%, specificity 78%, negative predictive value 85% and overall accuracy 76% for LVSD. AUC values were higher for patients aged <70 years (0.85 versus 0.80), males (0.84 versus 0.79), patients without ACS (0.86 versus 0.80), and patients who did not undergo revascularization (0.84 versus 0.80).
conclusionsThe AI-ECG algorithm had very good discrimination for LVSD in this critically-ill CICU cohort with a high prevalence of LVSD. Performance was better in younger male patients and those without ACS, highlighting those CICU patients in whom screening for LVSD using AI ECG may be more effective. The AI-ECG might potentially be useful for identification of LVSD in resource-limited settings when TTE is unavailable.
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