Observational studyEuropean journal of heart failure2025
Artificial intelligence fully automated analysis of handheld echocardiography in real-world patients with suspected heart failure.
Observational study in European journal of heart failure, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Point-of-care echocardiography screening for hypertrophic cardiomyopathy using automated deep-learning analysis.European heart journal. Digital health · 2026Article
- Reinventing the echocardiography workflow: from manual quantification to artificial intelligence-driven comprehensive interpretation.Journal of cardiovascular imaging · 2026Review
- Innovative strategies to reduce heart failure hospitalizations: insights from the ESC heart failure congress 2026.Heart failure reviews · 2026Review
- Diagnostic Tests for Stage B Heart Failure.Current cardiology reports · 2026Review
- Congestion and renal function in patients with chronic heart failure.ESC heart failure · 2026Article
- Post-COVID-19 era heart failure diagnosis and outcomes: adherence to National Institute for Health and Care Excellence Guidelines.Open heart · 2026Article
- AI-based clinical prediction model for early infectious disease classification in the emergency department.Frontiers in artificial intelligence · 2026Article
Corrections and comments
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Authors and funding
33 authors.
Funding
Abstract
aimsEchocardiography is a rate-limiting step in the timely diagnosis of heart failure (HF). Automated reporting of echocardiograms has the potential to streamline workflow. The aim of this study was to test the diagnostic accuracy of fully automated artificial intelligence (AI) analysis of images acquired using handheld echocardiography and its interchangeability with expert human-analysed cart-based echocardiograms in a real-world cohort with suspected HF. METHODS AND
resultsIn this multicentre, prospective, observational study, patients with suspected HF had two echocardiograms: one handheld portable and one cart-based scan. Both echocardiograms were analysed using fully automated AI software and by human expert sonographers. The primary endpoint was the diagnostic accuracy of AI-automated analysis of handheld echocardiography to detect left ventricular ejection fraction (LVEF) ≤40%. Other endpoints included the interchangeability (assessed using individual equivalence coefficient [IEC]), between AI-automated and human analysis of cart-based LVEF. A total of 867 patients participated. The AI-automated analysis produced an LVEF in 61% of the handheld scans and 77% of the cart-based scans, compared to 76% and 77% of human analyses of the handheld and cart-based scans, respectively. The AI-automated analysis of handheld echocardiography had a diagnostic accuracy of 0.93 (95% confidence interval [CI] 0.90, 0.95) for identifying LVEF ≤40% (compared to the human analysis of cart-based transthoracic echocardiography scans). AI-automated analysis of LVEF on handheld devices was interchangeable with cart-based LVEF reported by two expert humans (IEC -0.40, 95% CI -0.60, -0.16).
conclusionsArtificial intelligence-automated analysis of handheld echocardiography had good diagnostic accuracy for detecting LVEF ≤40%. AI-automated analysis of LVEF of handheld scans was interchangeable with cart-based expert human analysis.
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