ArticleEuropean heart journal. Imaging methods and practice2023
Real-time guiding by deep learning during echocardiography to reduce left ventricular foreshortening and measurement variability.
Article in European heart journal. Imaging methods and practice, 2023. 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 6 papers.
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
6 citing papers in PubMed.
- Real-time deep learning-based image guiding and automated left ventricular measurements to reduce test-retest variability.Open heart · 2025Trial
- Deep Learning for Cardiac Wall Motion Analysis: A Review of Methods, Challenges, and Clinical Applications.Annals of biomedical engineering · 2026Review
- Automated quality indicators for echocardiographic recordings and measurements: consequences for left ventricular global longitudinal strain.European heart journal. Imaging methods and practice · 2026Article
- Artificial intelligence-enhanced echocardiography in cardiovascular disease management.Nature reviews. Cardiology · 2026Review
- Real-time guidance and automated measurements using deep learning to improve echocardiographic assessment of left ventricular size and function.European heart journal. Imaging methods and practice · 2025Article
- Real-time guidance by deep learning of experienced operators to improve the standardization of echocardiographic acquisitions.European heart journal. Imaging methods and practice · 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
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aims: Apical foreshortening leads to an underestimation of left ventricular (LV) volumes and an overestimation of LV ejection fraction and global longitudinal strain. Real-time guiding using deep learning (DL) during echocardiography to reduce foreshortening could improve standardization and reduce variability. We aimed to study the effect of real-time DL guiding during echocardiography on measures of LV foreshortening and inter-observer variability. Methods and results: Patients ( Conclusion: Real-time guiding reduced foreshortening among experienced operators and has the potential to improve image standardization. Even though the effect on inter-operator variability was minimal among experienced users, real-time guiding may improve test-retest variability among less experienced users. Clinical trial registration: ClinicalTrials.gov, Identifier: 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.