An attention-based deep learning method for right ventricular quantification using 2D echocardiography: Feasibility and accuracy

Kampaktsis, P. N., Bohoran, T. A., Lebehn, M., McLaughlin, L., Leb, J., Liu, Z., Moustakidis, S., Siouras, A., Singh, A., Hahn, R. T., McCann, G. P., & Giannakidis, A. (2023). An attention‐based deep learning method for right ventricular quantification using 2D echocardiography: Feasibility and accuracy. Echocardiography, 41(1). Portico. https://doi.org/10.1111/echo.15719
Authors:
Polydoros N Kampaktsis
Tuan A Bohoran
Mark Lebehn
Laura McLaughlin
Jay Leb
Zhonghua Liu
Serafeim Moustakidis
Athanasios Siouras
Anvesha Singh
Rebecca T Hahn
Gerry P McCann
Archontis Giannakidis
Affiliated Authors:
Polydoros N Kampaktsis
Mark Lebehn
Laura McLaughlin
Jay Leb
Zhonghua Liu
Rebecca T Hahn
Columbia Affiliation:
Subjects:
Deep Learning (MeSH)
Ventricular Dysfunction, Right (MeSH)
Author Keywords:
deep learning
echocardiography
machine learning
quantification
right ventricle
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Publication Type:
Article
Unique ID:
10.1111/echo.15719
PMID:
38126261
DOI:
10.1111/echo.15719
Journal:
Echocardiography
Publication Date:
Data Source:
PubMed
Source Link:
View PubMed Record
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Record Created: