Diagnostic accuracy of artificial-intelligence-based electrocardiogram algorithm to estimate heart failure with reduced ejection fraction: A systematic review and meta-analysis
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André Luiz Carvalho Ferreira1, Luanna Paula Garcez de Carvalho Feitoza2, Maria E Benitez3
1Pontifical Catholic University of Paraná, Curitiba, Brazil.
Insights
Artificial intelligence (AI) electrocardiograms show high accuracy in diagnosing heart failure with reduced ejection fraction (LVEF <40%). This meta-analysis confirms AI ECGs reliably estimate low ejection fraction, aiding clinical decisions.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Diagnostics
Background:
- AI-based ECGs show promise for heart failure diagnosis.
- Precision for detecting reduced ejection fraction (LVEF <40%) requires further validation.
- Existing studies vary in cutoff points, impacting diagnostic accuracy.
Purpose of the Study:
- To evaluate the accuracy of AI ECGs in identifying reduced ejection fraction (LVEF <40%).
- To conduct a systematic review and meta-analysis of AI ECG performance for heart failure with reduced ejection fraction.
Main Methods:
- Systematic search of PubMed, Embase, and Cochrane Library for relevant studies.
- Meta-analysis of AI ECG performance using pooled sensitivity, specificity, and AUC.
- Inclusion of studies with paired AI-ECG and echocardiography data.
Main Results:
- Nine studies were included, comparing AI ECGs with echocardiography.
- AI ECGs demonstrated high pooled sensitivity and specificity for detecting LVEF <40%.
- The mean/median patient age ranged from 60±9 to 68.05±11.9 years.
Conclusions:
- AI-ECG models exhibit high sensitivity and specificity for estimating LVEF <40%.
- These findings support the clinical utility of AI ECGs in diagnosing heart failure with reduced ejection fraction.
Introduction:
AI-based ECG has shown good accuracy in diagnosing heart failure. However, due to the heterogeneity of studies regarding cutoff points, its precision for specifically detecting heart failure with left ventricle reduced ejection fraction (LVEF <40 %) is not yet well established. What is the sensitivity and specificity of artificial-based electrocardiogram to diagnose heart failure with low ejection fraction (cut-off of 40 %.
Aims:
We conducted a meta-analysis and systematic review to evaluate the accuracy of artificial intelligence electrocardiograms in estimating an ejection fraction below 40 %.
Methods:
We searched PubMed, Embase, and Cochrane Library for studies evaluating the performance of AI ECGs in diagnosing heart failure with reduced ejection fraction. We computed true positives, true negatives, false positives, and false negatives events to estimate pooled sensitivity, specificity, and area under the curve, using R software version 4.3.1, under a random-effects model.
Results:
We identified 9 studies, including patients with a paired artificial intelligence-enabled electrocardiogram with an echocardiography. patients had an ejection fraction below 40 % according to the echocardiogram. The AI-ECG data yielded areas under the receiver operator of, the sensitivity of), specificity of, and area under the curve of. The mean/median age ranged from 60±9 to 68.05± 11.9 years.
Conclusions:
In this systematic review and meta-analysis, the use of electrocardiogram-based artificial intelligence models demonstrated high sensitivity and specificity to estimate a left ventricular ejection fraction below 40 %.
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