Observational studyCritical care (London, England)2022
Machine learning for the real-time assessment of left ventricular ejection fraction in critically ill patients: a bedside evaluation by novices and experts in echocardiography.
Observational study in Critical care (London, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05336448 (POCUS Artificial Intelligence With Portable and Ultraportable Ultrasound Devices in Critically Ill Patients), which is not on this map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
POCUS Artificial Intelligence With Portable and Ultraportable Ultrasound Devices in Critically Ill Patients
Who cites it
15 citing papers in PubMed, 31 citations in OpenAlex.
- AI-augmented cardiopulmonary point-of-care ultrasound: a narrative review for pulmonary and critical care clinicians.Breathe (Sheffield, England) · 2026Article
- Access to haemodynamic evaluation tools in middle-income countries: a survey of 1593 anaesthetists and intensivists from 39 nations.BJA open · 2026Article
- AI-Assisted LVEF Assessment Using a Handheld Ultrasound Device: A Single-Center Comparative Study Against Cardiac Magnetic Resonance Imaging.Journal of clinical medicine · 2025Article
- Left ventricular systolic longitudinal strain in mechanically ventilated patients in the intensive care unit: assessment of global and chamber reproducibility.Intensive care medicine experimental · 2025Article
- Leveraging Comprehensive Echo Data to Power Artificial Intelligence Models for Handheld Cardiac Ultrasound.Mayo Clinic proceedings. Digital health · 2025Article
- AI for the hemodynamic assessment of critically ill and surgical patients: focus on clinical applications.Annals of intensive care · 2025Review
- Visualizing hemodynamics: innovative graphical displays and imaging techniques in anesthesia and critical care.Critical care (London, England) · 2025Review
- Clinical Relevance of Senior-Supervised Transthoracic Echocardiography in Clinical Practice and Research: An Editorial Commentary and Systematic Review.Echocardiography (Mount Kisco, N.Y.) · 2025Article
- To B or not to B-lines.Journal of anesthesia, analgesia and critical care · 2024Article
- Prospective Human Validation of Artificial Intelligence Interventions in Cardiology: A Scoping Review.JACC. Advances · 2024Article
- Continuous monitoring of left ventricular function in postoperative intensive care patients using artificial intelligence and transesophageal echocardiography.Intensive care medicine experimental · 2024Article
- Association of cardiovascular-kidney-metabolic index with all-cause mortality during hospitalization in critically ill patients: a retrospective cohort study from MIMIC IV2.2.Frontiers in cardiovascular medicine · 2024Article
- Letter to: Utility of pulmonary echography with a handheld ultrasound device in patients with acute coronary syndrome.Internal and emergency medicine · 2023Article
- Real-time guidance by deep learning of experienced operators to improve the standardization of echocardiographic acquisitions.European heart journal. Imaging methods and practice · 2023Article
- Artificial Intelligence (AI) versus POCUS Expert: A Validation Study of Three Automatic AI-Based, Real-Time, Hemodynamic Echocardiographic Assessment Tools.Journal of clinical medicine · 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 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMachine learning algorithms have recently been developed to enable the automatic and real-time echocardiographic assessment of left ventricular ejection fraction (LVEF) and have not been evaluated in critically ill patients.
methodsReal-time LVEF was prospectively measured in 95 ICU patients with a machine learning algorithm installed on a cart-based ultrasound system. Real-time measurements taken by novices (LVEF
resultsLVEF
conclusionMachine learning-enabled real-time measurements of LVEF were strongly correlated with manual measurements obtained by experts. The accuracy of real-time LVEF measurements was excellent, and the precision was fair. The reproducibility of LVEF measurements was better with the machine learning system. The specificity to detect left ventricular dysfunction was excellent both for experts and for novices, whereas the sensitivity could be improved.
trial registrationNCT05336448. Retrospectively registered on April 19, 2022.
Indexed as
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.