ArticleJournal of geriatric cardiology : JGC2022
Electrocardiogram-based artificial intelligence for the diagnosis of heart failure: a systematic review and meta-analysis.
Article in Journal of geriatric cardiology : JGC, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 16 citations in OpenAlex.
- Comparing machine learning models and traditional approaches for predicting obstructive coronary artery disease: a systematic review and meta-analysis.Frontiers in cardiovascular medicine · 2026Pooled it
- The Performance of Wearable AI in Detecting Stress Among Students: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled it
- Artificial intelligence-enabled electrocardiography for detection of left ventricular diastolic dysfunction: a systematic review and meta-analysis.European heart journal. Digital health · 2026Review
- Artificial Intelligence-Driven Electrocardiogram Screening for Asymptomatic Left Ventricular Systolic Dysfunction in the General Population.JACC. Advances · 2026Article
- A digital diagnostic pathway for heart failure: an economic evaluation.Cost effectiveness and resource allocation : C/E · 2026Article
- Artificial intelligence in early heart failure detection: from algorithms to bedside integration.Frontiers in cardiovascular medicine · 2026Article
- Artificial Intelligence-Enabled Electrocardiogram for the Detection and Management of Cancer Therapy-Related Cardiotoxicity.Cancer innovation · 2025Article
- [Artificial intelligence and cardiology in Latin America: a 5-year bibliometric analysis of scientific research].Archivos de cardiologia de Mexico · 2025Article
- Emerging rapid detection methods for the monitoring of cardiovascular diseases: Current trends and future perspectives.Materials today. Bio · 2025Review
- Use of Artificial Intelligence Applied to Electrocardiogram for Diagnosis of Left Ventricular Systolic Dysfunction.Arquivos brasileiros de cardiologia · 2025Article
- Efficacy of AI Models in Detecting Heart Failure Using ECG Data: A Systematic Review and Meta-Analysis.Cureus · 2025Review
- A multitask deep learning model utilizing electrocardiograms for major cardiovascular adverse events prediction.NPJ digital medicine · 2025Article
- Interoception, cardiac health, and heart failure: The potential for artificial intelligence (AI)-driven diagnosis and treatment.Physiological reports · 2025Review
- A progressive attention-based cross-modal fusion network for cardiovascular disease detection using synchronized electrocardiogram and phonocardiogram signals.PeerJ. Computer science · 2025Article
- Benefits of Information Technology in Healthcare: Artificial Intelligence, Internet of Things, and Personal Health Records.Healthcare informatics research · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe electrocardiogram (ECG) is an inexpensive and easily accessible investigation for the diagnosis of cardiovascular diseases including heart failure (HF). The application of artificial intelligence (AI) has contributed to clinical practice in terms of aiding diagnosis, prognosis, risk stratification and guiding clinical management. The aim of this study is to systematically review and perform a meta-analysis of published studies on the application of AI for HF detection based on the ECG.
methodsWe searched Embase, PubMed and Web of Science databases to identify literature using AI for HF detection based on ECG data. The quality of included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) criteria. Random-effects models were used for calculating the effect estimates and hierarchical receiver operating characteristic curves were plotted. Subgroup analysis was performed. Heterogeneity and the risk of bias were also assessed.
resultsA total of 11 studies including 104,737 subjects were included. The area under the curve for HF diagnosis was 0.986, with a corresponding pooled sensitivity of 0.95 (95% CI: 0.86-0.98), specificity of 0.98 (95% CI: 0.95-0.99) and diagnostic odds ratio of 831.51 (95% CI: 127.85-5407.74). In the patient selection domain of QUADAS-2, eight studies were designated as high risk.
conclusionsAccording to the available evidence, the incorporation of AI can aid the diagnosis of HF. However, there is heterogeneity among machine learning algorithms and improvements are required in terms of quality and study design.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.