ArticleEuropean heart journal. Digital health2024
Nurse-led home-based detection of cardiac dysfunction by ultrasound: results of the CUMIN pilot study.
Article in European heart journal. Digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06944405 (Implementation for Heart Failure Therapies Post-discharge Followed by CardiOSIgnal at HOME), which is not on this map. Cited by 10 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.
Implementation for Heart Failure Therapies Post-discharge Followed by CardiOSIgnal at HOME
Who cites it
10 citing papers in PubMed, 16 citations in OpenAlex.
- AI-augmented cardiopulmonary point-of-care ultrasound: a narrative review for pulmonary and critical care clinicians.Breathe (Sheffield, England) · 2026Article
- Decentralized community-based hub-intermediary-spoke model for rapid cardiac ultrasound triage for early heart failure detection: findings from the Heart2Miss initiative.European heart journal. Digital health · 2026Article
- Artificial Intelligence in Cardiac Point-of-Care Ultrasound: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- AI task-shifting for echocardiographic LVEF assessment in Singapore: an economic evaluation.ESC heart failure · 2026Article
- A perspective on AI implementation in medical imaging in LMICs: challenges, priorities, and strategies.European radiology · 2026Article
- Artificial intelligence-enhanced echocardiography in cardiovascular disease management.Nature reviews. Cardiology · 2026Review
- Finding and Managing Heart Failure Earlier: Why Enhancing Primary Care Teams to Apply More Definitive Surveillance is the Solution.Cardiac failure review · 2026Review
- Large language models and global health equity: a roadmap for equitable adoption in LMICs.The Lancet regional health. Western Pacific · 2025Review
- From Echocardiography to CT/MRI: Lessons for AI Implementation in Cardiovascular Imaging in LMICs-A Systematic Review and Narrative Synthesis.Bioengineering (Basel, Switzerland) · 2025Review
- Machine learning in the prevention of heart failure.Heart failure reviews · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 6 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aims: Access to echocardiography is a significant barrier to heart failure (HF) care in many low- and middle-income countries. In this study, we hypothesized that an artificial intelligence (AI)-enhanced point-of-care ultrasound (POCUS) device could enable the detection of cardiac dysfunction by nurses in Tunisia. Methods and results: This CUMIN study was a prospective feasibility pilot assessing the diagnostic accuracy of home-based AI-POCUS for HF conducted by novice nurses compared with conventional clinic-based transthoracic echocardiography (TTE). Seven nurses underwent a one-day training program in AI-POCUS. A total of 94 patients without a previous HF diagnosis received home-based AI-POCUS, POC N-terminal pro-B-type natriuretic peptide (NT-proBNP) testing, and clinic-based TTE. The primary outcome was the sensitivity of AI-POCUS in detecting a left ventricular ejection fraction (LVEF) <50% or left atrial volume index (LAVI) >34 mL/m Conclusion: The study demonstrated the feasibility of novice nurse-led home-based detection of cardiac dysfunction using AI-POCUS in HF patients, which could alleviate the burden on under-resourced healthcare systems.
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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.