ArticleCHEST pulmonary2023
Acquisition of Cardiac Point-of-Care Ultrasound Images With Deep Learning: A Randomized Trial for Educational Outcomes With Novices.
Article in CHEST pulmonary, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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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.
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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.
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Who cites it
21 citing papers in PubMed.
- AI-enhanced guidance demonstrated improvement in novices' Apical-4-chamber and Apical-5-chamber views.BMC medical education · 2025Trial
- Artificial intelligence in cardiovascular care: a systematic review and meta-analysis of randomised controlled trials.EClinicalMedicine · 2026Article
- Role of artificial intelligence and point of care ultrasound in management of critically ill patients.World journal of critical care medicine · 2026Review
- Impact of artificial intelligence on cardiovascular workflow, engagement, and outcomes: a systematic review.NPJ digital medicine · 2026Article
- Artificial intelligence in breaking the learning curve for echocardiography: a secondary analysis of a multicentre trial.European heart journal. Digital health · 2026Article
- Creation of an Open-Access Lung POCUS Image Database for Deep Learning and Neural Network Applications.POCUS journal · 2026Article
- Innovation, Technology, and Telemedicine in Critical Care.Critical care clinics · 2026Review
- Development of a Deep Learning-Based Feedback Model to Assist Medical Students Learning Renal Ultrasound Acquisition: Mixed Methods Study.JMIR medical education · 2026Article
- Artificial intelligence-enhanced echocardiography in cardiovascular disease management.Nature reviews. Cardiology · 2026Review
- Artificial Intelligence Assistance Does Not Provide Immediate Benefit for Right Upper Quadrant Window Acquisition.Cureus · 2026Article
- The evolution and future of point of care ultrasound in the perioperative period: narrative review.Journal of clinical monitoring and computing · 2026Review
- Artificial Intelligence Improves Apical Four-Chamber Window Quality in Experienced but Not Novice Users.Cureus · 2026Article
- AI-Assisted Handheld Echocardiography by Nonexpert Operators: A Narrative Review of Prospective Studies.Cureus · 2025Review
- Artificial Intelligence-Enabled Point-of-Care Echocardiography: Bringing Precision Imaging to the Bedside.Current atherosclerosis reports · 2025Review
- Clinical obstacles to machine-learning POCUS adoption and system-wide AI implementation (The COMPASS-AI survey).The ultrasound journal · 2025Article
- AI assisted focused cardiac ultrasound in preventive cardiology - a perspective.NPJ cardiovascular health · 2025Review
- National Cost Savings From Use of Artificial Intelligence Guided Echocardiography in the Assessment of Intermediate-Risk Patients With Syncope in the Emergency Department.Journal of the American College of Emergency Physicians open · 2025Article
- Continuing professional development for primary care physicians: a pre-post analysis of a focused abdominal point-of-care ultrasound pilot training.BMC medical education · 2025Article
- Limited echocardiogram acquisition by novice clinicians aided with deep learning: A randomized controlled trial.Biology methods & protocols · 2025Article
- Optimising artificial intelligence ultrasound tools in anaesthesiology and perioperative medicine: The next frontier for advanced technology application.Indian journal of anaesthesia · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Point-of-care ultrasonography (POCUS) machines may use deep learning, a subfield of artificial intelligence (AI), to improve image interpretation and acquisition in real time. The impact of AI on POCUS learning is unknown. Research Question: Do AI-enhanced devices equipped with deep learning aid in cardiac image acquisition and interpretation among POCUS novices? Study Design and Methods: We conducted a single-center investigation from 2021 through 2022. Internal medicine trainees (N = 43) with limited POCUS experience were randomized to receive a POCUS device with (Echonous; n = 22) or without (Butterfly; n = 21) AI functionality for 2 weeks while on inpatient rotations. AI device functionality included guidance for optimal probe placement to acquire an apical four-chamber (A4C) view and ejection fraction estimations based on deep learning. Participants used the devices at their discretion for patient-related care after randomization. The primary outcome was the time to acquire A4C images on a standardized patient. Secondary outcomes included A4C image quality using a validated scale, image quiz performance, and attitudes. Measurements were performed at randomization and at 2-week follow-up using the same standardized patient. Results: Both AI and non-AI groups showed similar scan times and image quality scores at baseline. At follow-up, the AI group showed faster scan times (57 s [interquartile range (IQR), 32-75 s] vs 85 s [IQR, 50-172 s]; Interpretation: POCUS devices with deep learning functionality may improve A4C image acquisition and interpretation by novices. Future studies are needed to determine the extent that AI impacts POCUS learning.
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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.