Trial reportOpen heart2025
Real-time deep learning-based image guiding and automated left ventricular measurements to reduce test-retest variability.
Trial report in Open heart, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04580095 (Technology Revisiting Ultrasound Solutions of Tomorrow - Artificial Intelligence for Improved Echocardiography), which is not on this map. Cited by 1 paper.
What it found
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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.
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.
Technology Revisiting Ultrasound Solutions of Tomorrow - Artificial Intelligence for Improved Echocardiography
Who cites it
1 citing paper in PubMed.
- Deep Learning for Cardiac Wall Motion Analysis: A Review of Methods, Challenges, and Clinical Applications.Annals of biomedical engineering · 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
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsTo evaluate the effect of combining real-time deep learning (DL)-based guiding and automated measurements of left ventricular (LV) volumetric measurements and strain. METHODS AND
resultsPatients (n=47) with mixed cardiac pathology were examined by two sonographers and one reference cardiologist. A real-time DL guiding tool to avoid LV foreshortening was used by one sonographer only per patient. Automated DL-based measurements from the sonographer using the guiding tool were paired with automated measurements from the reference cardiologist (artificial intelligence (AI)-assisted echocardiography), while manual measurements from the sonographer not using the guiding tool were paired with manual measurements from the reference cardiologist (standard echocardiography). The variability of LV EDV, LV ESV, ejection fraction (LV EF) and global longitudinal strain (LV GLS) was compared for standard echocardiography versus AI-assisted echocardiography. Coefficients of variation were lower for AI-assisted echocardiography compared with standard echocardiography (6% vs 15% for LV EDV (p<0.001), 10% vs 19% for ESV (p<0.001) and 7% vs 11% for GLS (p=0.047), respectively). For LV EF, the coefficients of variation were similar across groups (8% vs 9%, p=0.503, respectively). In exploratory analyses, automated measurements alone (all p≤0.002) but not the guiding tool (all ≥0.199) explained the improved variability for LV EDV, ESV and GLS.
conclusionsAI-assisted echocardiography combining DL-based real-time guiding and automated measurements significantly reduced the variability of LV EDV, ESV and GLS when compared to standard echocardiography. Among experienced operators, automated measurements were more beneficial than real-time guiding. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov, ID: NCT04580095.
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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.