ArticlePloS one2023
Left ventricle segmentation in transesophageal echocardiography images using a deep neural network.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the 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.
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Who cites it
6 citing papers in PubMed, 13 citations in OpenAlex.
- Effects of intraoperative open-lung strategy on cardiopulmonary function in frail elderly patients undergoing laparoscopic surgery: a randomized controlled trial.BMC surgery · 2025Trial
- Automatic Detection of Vegetations With Transesophageal Echocardiography in Infective Endocarditis Using Artificial Intelligence.Journal of imaging informatics in medicine · 2026Observational
- "ShapeNet": A Shape Regression Convolutional Neural Network Ensemble Applied to the Segmentation of the Left Ventricle in Echocardiography.Journal of imaging · 2025Article
- Automatic Segmentation of the Left Ventricle in Apical Four-Chamber View on Transesophageal Echocardiography Based on UNeXt Deep Neural Network.Diagnostics (Basel, Switzerland) · 2024Article
- Revolutionizing Cardiac Imaging: A Scoping Review of Artificial Intelligence in Echocardiography, CTA, and Cardiac MRI.Journal of imaging · 2024Article
- Deep Learning Based Automatic Left Ventricle Segmentation from the Transgastric Short-Axis View on Transesophageal Echocardiography: A Feasibility Study.Diagnostics (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThere has been little progress in research on the best anatomical position for effective chest compressions and cardiac function during cardiopulmonary resuscitation (CPR). This study aimed to divide the left ventricle (LV) into segments to determine the best position for effective chest compressions using the LV systolic function seen during CPR.
methodsWe used transesophageal echocardiography images acquired during CPR. A deep neural network with an attention mechanism and a residual feature aggregation module were applied to the images to segment the LV. The results were compared between the proposed model and U-Net.
resultsThe results of the proposed model showed higher performance in most metrics when compared to U-Net: dice coefficient (0.899±0.017 vs. 0.792±0.027, p<0.05); intersection of union (0.822±0.026 vs. 0.668±0.034, p<0.05); recall (0.904±0.023 vs. 0.757±0.037, p<0.05); precision (0.901±0.021 vs. 0.859±0.034, p>0.05). There was a significant difference between the proposed model and U-Net.
conclusionCompared to U-Net, the proposed model showed better performance for all metrics. This model would allow us to evaluate the systolic function of the heart during CPR in greater detail by segmenting the LV more accurately.
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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.