ArticleEuropean heart journal. Digital health2022
Clinical validation of an artificial intelligence-assisted algorithm for automated quantification of left ventricular ejection fraction in real time by a novel handheld ultrasound device.
Article in European heart journal. Digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.
- PRIME 2.0: Proposed Requirements for Cardiovascular Imaging-Related Multimodal-AI Evaluation: An Updated Checklist.JACC. Cardiovascular imaging · 2026Guideline
- Trial
- Handheld Ultrasound: Technical Review and International Expert Consensus Recommendations.Diagnostics (Basel, Switzerland) · 2026Review
- AI-augmented cardiopulmonary point-of-care ultrasound: a narrative review for pulmonary and critical care clinicians.Breathe (Sheffield, England) · 2026Article
- Validation of a fully automated workflow for longitudinal strain using artificial intelligence.Journal of echocardiography · 2026Article
- A view-flexible deep learning framework for automated analysis of 2D echocardiography.NPJ cardiovascular health · 2026Article
- Application and prospect of artificial intelligence in diagnostic imaging of prostate cancer.NPJ digital medicine · 2026Review
- Diagnostic accuracy and cost-effectiveness of a handheld ultrasound device for cardiac evaluation in noncardiology settings.European heart journal. Imaging methods and practice · 2026Article
- Artificial intelligence in echocardiography: current applications and future perspectives.Journal of echocardiography · 2025Review
- Diagnostic Accuracy of AI-Assisted Focused Cardiac Ultrasound (FOCUS) in Primary Care.Healthcare (Basel, Switzerland) · 2025Article
- AI-Assisted LVEF Assessment Using a Handheld Ultrasound Device: A Single-Center Comparative Study Against Cardiac Magnetic Resonance Imaging.Journal of clinical medicine · 2025Article
- Overcoming barriers in the use of artificial intelligence in point of care ultrasound.NPJ digital medicine · 2025Review
- Leveraging Comprehensive Echo Data to Power Artificial Intelligence Models for Handheld Cardiac Ultrasound.Mayo Clinic proceedings. Digital health · 2025Article
- Artificial Intelligence in Clinics: Enhancing Cardiology Practice.JMA journal · 2025Review
- Reducing echocardiographic examination time through routine use of fully automated software: a comparative study of measurement and report creation time.Journal of echocardiography · 2024Article
- Assessment of an Artificial Intelligence Tool for Estimating Left Ventricular Ejection Fraction in Echocardiograms from Apical and Parasternal Long-Axis Views.Diagnostics (Basel, Switzerland) · 2024Article
- Multicenter validation study for automated left ventricular ejection fraction assessment using a handheld ultrasound with artificial intelligence.Scientific reports · 2024Article
- Artificial intelligence-assisted evaluation of cardiac function by oncology staff in chemotherapy patients.European heart journal. Digital health · 2024Article
- Automatic measurements of left ventricular volumes and ejection fraction by artificial intelligence: clinical validation in real time and large databases.European heart journal. Cardiovascular Imaging · 2024Article
- Experience with the Kosmos ultrasonographic tool in the approach and treatment of ambulatory patients of a heart failure clinic: a single-center cross-sectional study.Archivos de cardiologia de Mexico · 2024Observational
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aims: We sought to evaluate the reliability and diagnostic accuracy of a novel handheld ultrasound device (HUD) with artificial intelligence (AI) assisted algorithm to automatically calculate ejection fraction (autoEF) in a real-world patient population. Methods and results: We studied 100 consecutive patients (57 ± 15 years old, 61% male), including 38 with abnormal left ventricular (LV) function [LV ejection fraction (LVEF) < 50%]. The autoEF results acquired using the HUD were independently compared with manually traced biplane Simpson's rule measurements on cart-based systems to assess method agreement using intra-class correlation coefficient (ICC), linear regression analysis, and Bland-Altman analysis. The diagnostic accuracy for the detection of LVEF <50% was also calculated. Test-retest reliability of measured EF by the HUD was assessed by calculating the ICC and the minimal detectable change (MDC). The ICC, linear regression analysis, and Bland-Altman analysis revealed good agreement between autoEF and reference manual EF (ICC = 0.85; Conclusion: Use of a novel HUD with AI-enabled capabilities provided similar LVEF results with those derived by manual biplane Simpson's method on cart-based systems and shows clinical potential.
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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.