ArticleBiology methods & protocols2025
Limited echocardiogram acquisition by novice clinicians aided with deep learning: A randomized controlled trial.
Article in Biology methods & protocols, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Artificial intelligence in cardiovascular care: a systematic review and meta-analysis of randomised controlled trials.EClinicalMedicine · 2026Article
- Impact of artificial intelligence on cardiovascular workflow, engagement, and outcomes: a systematic review.NPJ digital medicine · 2026Article
- Beyond Computer-Aided Diagnosis: Artificial Intelligence as a "Digital Mentor" for POCUS Image Acquisition and Quality Assurance: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial intelligence and digital health in heart failure: advances in diagnosis, monitoring, phenotyping, and digital biomarkers.Heart failure reviews · 2026Review
- Quantum-edge synergy for dynamic acoustic compensation in ultrasound safety: a narrative review of tissue heterogeneity challenges.Frontiers in medical technology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The global shortage of sonographers has created significant barriers to timely ultrasound diagnostics across medical specialties. Deep learning (DL) algorithms have potential to enhance image acquisition by clinicians without formal sonography training, potentially expanding access to crucial diagnostic imaging in resource-limited settings. This study evaluates whether DL-enabled devices improve acquisition of multi-view limited echocardiograms by healthcare providers without previous cardiac ultrasound training. In this single-center randomized controlled trial (2023-2024), internal medicine residents (
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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.