Deep learning for automated left ventricular outflow tract diameter measurements in 2D echocardiography

Deep learning for automated left ventricular outflow tract diameter measurements in 2D echocardiography

Abstract Background Measurement of the left ventricular outflow tract diameter (LVOTd) in echocardiography is a common source of error when used to calculate the stroke volume. The aim of this study is to assess whether a deep learning (DL) model, trained on a clinical echocardiographic dataset, can...

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Bibliographic Details
Main Authors: Sigurd Zijun Zha, Magnus Rogstadkjernet, Lars Gunnar Klæboe, Helge Skulstad, Bjørn-Jostein Singstad, Andrew Gilbert, Thor Edvardsen, Eigil Samset, Pål Haugar Brekke
Format: Article
Language:English
Published: BMC 2023-10-01
Series:Cardiovascular Ultrasound
Subjects:
Deep learning
Left ventricular outflow tract
Transthoracic echocardiography
Machine learning
Automated measurements
Online Access:https://doi.org/10.1186/s12947-023-00317-5

Internet

https://doi.org/10.1186/s12947-023-00317-5

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