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Fast and accurate view classification of echocardiograms using deep learning

Echocardiography is essential to cardiology. However, the need for human interpretation has limited echocardiography’s full potential for precision medicine. Deep learning is an emerging tool for analyzing images but has not yet been widely applied to echocardiograms, partly due to their complex mul...

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Autores principales: Madani, Ali, Arnaout, Ramy, Mofrad, Mohammad, Arnaout, Rima
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6395045/
https://www.ncbi.nlm.nih.gov/pubmed/30828647
http://dx.doi.org/10.1038/s41746-017-0013-1
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author Madani, Ali
Arnaout, Ramy
Mofrad, Mohammad
Arnaout, Rima
author_facet Madani, Ali
Arnaout, Ramy
Mofrad, Mohammad
Arnaout, Rima
author_sort Madani, Ali
collection PubMed
description Echocardiography is essential to cardiology. However, the need for human interpretation has limited echocardiography’s full potential for precision medicine. Deep learning is an emerging tool for analyzing images but has not yet been widely applied to echocardiograms, partly due to their complex multi-view format. The essential first step toward comprehensive computer-assisted echocardiographic interpretation is determining whether computers can learn to recognize these views. We trained a convolutional neural network to simultaneously classify 15 standard views (12 video, 3 still), based on labeled still images and videos from 267 transthoracic echocardiograms that captured a range of real-world clinical variation. Our model classified among 12 video views with 97.8% overall test accuracy without overfitting. Even on single low-resolution images, accuracy among 15 views was 91.7% vs. 70.2–84.0% for board-certified echocardiographers. Data visualization experiments showed that the model recognizes similarities among related views and classifies using clinically relevant image features. Our results provide a foundation for artificial intelligence-assisted echocardiographic interpretation.
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spelling pubmed-63950452019-02-28 Fast and accurate view classification of echocardiograms using deep learning Madani, Ali Arnaout, Ramy Mofrad, Mohammad Arnaout, Rima NPJ Digit Med Article Echocardiography is essential to cardiology. However, the need for human interpretation has limited echocardiography’s full potential for precision medicine. Deep learning is an emerging tool for analyzing images but has not yet been widely applied to echocardiograms, partly due to their complex multi-view format. The essential first step toward comprehensive computer-assisted echocardiographic interpretation is determining whether computers can learn to recognize these views. We trained a convolutional neural network to simultaneously classify 15 standard views (12 video, 3 still), based on labeled still images and videos from 267 transthoracic echocardiograms that captured a range of real-world clinical variation. Our model classified among 12 video views with 97.8% overall test accuracy without overfitting. Even on single low-resolution images, accuracy among 15 views was 91.7% vs. 70.2–84.0% for board-certified echocardiographers. Data visualization experiments showed that the model recognizes similarities among related views and classifies using clinically relevant image features. Our results provide a foundation for artificial intelligence-assisted echocardiographic interpretation. Nature Publishing Group UK 2018-03-21 /pmc/articles/PMC6395045/ /pubmed/30828647 http://dx.doi.org/10.1038/s41746-017-0013-1 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Madani, Ali
Arnaout, Ramy
Mofrad, Mohammad
Arnaout, Rima
Fast and accurate view classification of echocardiograms using deep learning
title Fast and accurate view classification of echocardiograms using deep learning
title_full Fast and accurate view classification of echocardiograms using deep learning
title_fullStr Fast and accurate view classification of echocardiograms using deep learning
title_full_unstemmed Fast and accurate view classification of echocardiograms using deep learning
title_short Fast and accurate view classification of echocardiograms using deep learning
title_sort fast and accurate view classification of echocardiograms using deep learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6395045/
https://www.ncbi.nlm.nih.gov/pubmed/30828647
http://dx.doi.org/10.1038/s41746-017-0013-1
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