ArticleNPJ digital medicine2023
Diagnostic accuracy of point-of-care ultrasound with artificial intelligence-assisted assessment of left ventricular ejection fraction.
Article in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Artificial Intelligence Echocardiographic Interpretation in the Emergency Department: Agreement, Interpretation Yield and Reporting Scope.Emergency medicine Australasia : EMA · 2026Article
- The diagnostic accuracy of left ventricular ejection fraction assessment between visual and artificial intelligence-based algorithms on bedside ultrasound.Cardiovascular ultrasound · 2026Article
- Seven Pillars for a Community-Led AI-POCUS Future - A WINFOCUS Manifesto.The ultrasound journal · 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
- Role of artificial intelligence and point of care ultrasound in management of critically ill patients.World journal of critical care medicine · 2026Review
- Improving structural heart disease screening: AI-ECG and novice AI-guided focused cardiac ultrasound.NPJ digital medicine · 2026Article
- Smarter FoCUS: AI-guided focused cardiac ultrasound enables novice detection of left ventricular dysfunction.European heart journal. Digital health · 2026Article
- Artificial Intelligence-Enhanced Cardiac Point-of-Care Ultrasound: A Prospective Single-Arm Study.Mayo Clinic proceedings. Digital health · 2026Article
- Artificial intelligence-enhanced echocardiography in cardiovascular disease management.Nature reviews. Cardiology · 2026Review
- The effect of AI-assisted bedside echocardiography on inpatient care: a prospective trial.European heart journal. Digital health · 2026Article
- AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions.International journal of general medicine · 2026Review
- Beyond the chain of survival: a scoping review of artificial intelligence applications in cardiac arrest.World journal of emergency medicine · 2026Review
- AI-Assisted Handheld Echocardiography by Nonexpert Operators: A Narrative Review of Prospective Studies.Cureus · 2025Review
- Diagnostic Accuracy of AI-Assisted Focused Cardiac Ultrasound (FOCUS) in Primary Care.Healthcare (Basel, Switzerland) · 2025Article
- Echo contrast medium: How the use of contrast echocardiography (ultrasound contrast agents) can improve patient care.World journal of methodology · 2025Review
- Artificial Intelligence-Enabled Point-of-Care Echocardiography: Bringing Precision Imaging to the Bedside.Current atherosclerosis reports · 2025Review
- AI-Assisted LVEF Assessment Using a Handheld Ultrasound Device: A Single-Center Comparative Study Against Cardiac Magnetic Resonance Imaging.Journal of clinical medicine · 2025Article
- AI assisted focused cardiac ultrasound in preventive cardiology - a perspective.NPJ cardiovascular health · 2025Review
- Evaluating the digital health technology landscape in sub-Saharan Africa and its implications for cardiovascular health.NPJ cardiovascular health · 2025Review
- Overcoming barriers in the use of artificial intelligence in point of care ultrasound.NPJ digital medicine · 2025Review
Corrections and comments
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Authors and funding
17 authors.
Funding
Abstract
Focused cardiac ultrasound (FoCUS) is becoming standard practice in a wide spectrum of clinical settings. There is limited data evaluating the real-world use of FoCUS with artificial intelligence (AI). Our objective was to determine the accuracy of FoCUS AI-assisted left ventricular ejection fraction (LVEF) assessment and compare its accuracy between novice and experienced users. In this prospective, multicentre study, participants requiring a transthoracic echocardiogram (TTE) were recruited to have a FoCUS done by a novice or experienced user. The AI-assisted device calculated LVEF at the bedside, which was subsequently compared to TTE. 449 participants were enrolled with 424 studies included in the final analysis. The overall intraclass coefficient was 0.904, and 0.921 in the novice (n = 208) and 0.845 in the experienced (n = 216) cohorts. There was a significant bias of 0.73% towards TTE (p = 0.005) with a level of agreement of 11.2%. Categorical grading of LVEF severity had excellent agreement to TTE (weighted kappa = 0.83). The area under the curve (AUC) was 0.98 for identifying an abnormal LVEF (<50%) with a sensitivity of 92.8%, specificity of 92.3%, negative predictive value (NPV) of 0.97 and a positive predictive value (PPV) of 0.83. In identifying severe dysfunction (<30%) the AUC was 0.99 with a sensitivity of 78.1%, specificity of 98.0%, NPV of 0.98 and PPV of 0.76. Here we report that FoCUS AI-assisted LVEF assessments provide highly reproducible LVEF estimations in comparison to formal TTE. This finding was consistent among senior and novice echocardiographers suggesting applicability in a variety of clinical settings.
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Registered trials
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