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Standard Echocardiographic View Recognition in Diagnosis of Congenital Heart Defects in Children Using Deep Learning Based on Knowledge Distillation

Standard echocardiographic view recognition is a prerequisite for automatic diagnosis of congenital heart defects (CHDs). This study aims to evaluate the feasibility and accuracy of standard echocardiographic view recognition in the diagnosis of CHDs in children using convolutional neural networks (...

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Autores principales: Wu, Lanping, Dong, Bin, Liu, Xiaoqing, Hong, Wenjing, Chen, Lijun, Gao, Kunlun, Sheng, Qiuyang, Yu, Yizhou, Zhao, Liebin, Zhang, Yuqi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8805220/
https://www.ncbi.nlm.nih.gov/pubmed/35118028
http://dx.doi.org/10.3389/fped.2021.770182
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author Wu, Lanping
Dong, Bin
Liu, Xiaoqing
Hong, Wenjing
Chen, Lijun
Gao, Kunlun
Sheng, Qiuyang
Yu, Yizhou
Zhao, Liebin
Zhang, Yuqi
author_facet Wu, Lanping
Dong, Bin
Liu, Xiaoqing
Hong, Wenjing
Chen, Lijun
Gao, Kunlun
Sheng, Qiuyang
Yu, Yizhou
Zhao, Liebin
Zhang, Yuqi
author_sort Wu, Lanping
collection PubMed
description Standard echocardiographic view recognition is a prerequisite for automatic diagnosis of congenital heart defects (CHDs). This study aims to evaluate the feasibility and accuracy of standard echocardiographic view recognition in the diagnosis of CHDs in children using convolutional neural networks (CNNs). A new deep learning-based neural network method was proposed to automatically and efficiently identify commonly used standard echocardiographic views. A total of 367,571 echocardiographic image slices from 3,772 subjects were used to train and validate the proposed echocardiographic view recognition model where 23 standard echocardiographic views commonly used to diagnose CHDs in children were identified. The F1 scores of a majority of views were all ≥0.90, including subcostal sagittal/coronal view of the atrium septum, apical four-chamber view, apical five-chamber view, low parasternal four-chamber view, sax-mid, sax-basal, parasternal long-axis view of the left ventricle (PSLV), suprasternal long-axis view of the entire aortic arch, M-mode echocardiographic recording of the aortic (M-AO) and the left ventricle at the level of the papillary muscle (M-LV), Doppler recording from the mitral valve (DP-MV), the tricuspid valve (DP-TV), the ascending aorta (DP-AAO), the pulmonary valve (DP-PV), and the descending aorta (DP-DAO). This study provides a solid foundation for the subsequent use of artificial intelligence (AI) to identify CHDs in children.
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spelling pubmed-88052202022-02-02 Standard Echocardiographic View Recognition in Diagnosis of Congenital Heart Defects in Children Using Deep Learning Based on Knowledge Distillation Wu, Lanping Dong, Bin Liu, Xiaoqing Hong, Wenjing Chen, Lijun Gao, Kunlun Sheng, Qiuyang Yu, Yizhou Zhao, Liebin Zhang, Yuqi Front Pediatr Pediatrics Standard echocardiographic view recognition is a prerequisite for automatic diagnosis of congenital heart defects (CHDs). This study aims to evaluate the feasibility and accuracy of standard echocardiographic view recognition in the diagnosis of CHDs in children using convolutional neural networks (CNNs). A new deep learning-based neural network method was proposed to automatically and efficiently identify commonly used standard echocardiographic views. A total of 367,571 echocardiographic image slices from 3,772 subjects were used to train and validate the proposed echocardiographic view recognition model where 23 standard echocardiographic views commonly used to diagnose CHDs in children were identified. The F1 scores of a majority of views were all ≥0.90, including subcostal sagittal/coronal view of the atrium septum, apical four-chamber view, apical five-chamber view, low parasternal four-chamber view, sax-mid, sax-basal, parasternal long-axis view of the left ventricle (PSLV), suprasternal long-axis view of the entire aortic arch, M-mode echocardiographic recording of the aortic (M-AO) and the left ventricle at the level of the papillary muscle (M-LV), Doppler recording from the mitral valve (DP-MV), the tricuspid valve (DP-TV), the ascending aorta (DP-AAO), the pulmonary valve (DP-PV), and the descending aorta (DP-DAO). This study provides a solid foundation for the subsequent use of artificial intelligence (AI) to identify CHDs in children. Frontiers Media S.A. 2022-01-18 /pmc/articles/PMC8805220/ /pubmed/35118028 http://dx.doi.org/10.3389/fped.2021.770182 Text en Copyright © 2022 Wu, Dong, Liu, Hong, Chen, Gao, Sheng, Yu, Zhao and Zhang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Pediatrics
Wu, Lanping
Dong, Bin
Liu, Xiaoqing
Hong, Wenjing
Chen, Lijun
Gao, Kunlun
Sheng, Qiuyang
Yu, Yizhou
Zhao, Liebin
Zhang, Yuqi
Standard Echocardiographic View Recognition in Diagnosis of Congenital Heart Defects in Children Using Deep Learning Based on Knowledge Distillation
title Standard Echocardiographic View Recognition in Diagnosis of Congenital Heart Defects in Children Using Deep Learning Based on Knowledge Distillation
title_full Standard Echocardiographic View Recognition in Diagnosis of Congenital Heart Defects in Children Using Deep Learning Based on Knowledge Distillation
title_fullStr Standard Echocardiographic View Recognition in Diagnosis of Congenital Heart Defects in Children Using Deep Learning Based on Knowledge Distillation
title_full_unstemmed Standard Echocardiographic View Recognition in Diagnosis of Congenital Heart Defects in Children Using Deep Learning Based on Knowledge Distillation
title_short Standard Echocardiographic View Recognition in Diagnosis of Congenital Heart Defects in Children Using Deep Learning Based on Knowledge Distillation
title_sort standard echocardiographic view recognition in diagnosis of congenital heart defects in children using deep learning based on knowledge distillation
topic Pediatrics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8805220/
https://www.ncbi.nlm.nih.gov/pubmed/35118028
http://dx.doi.org/10.3389/fped.2021.770182
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