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Automatic Detection of Secundum Atrial Septal Defect in Children Based on Color Doppler Echocardiographic Images Using Convolutional Neural Networks
Secundum atrial septal defect (ASD) is one of the most common congenital heart diseases (CHDs). This study aims to evaluate the feasibility and accuracy of automatic detection of ASD in children based on color Doppler echocardiographic images using convolutional neural networks. In this study, we pr...
Autores principales: | , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9019069/ https://www.ncbi.nlm.nih.gov/pubmed/35463790 http://dx.doi.org/10.3389/fcvm.2022.834285 |
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author | Hong, Wenjing Sheng, Qiuyang Dong, Bin Wu, Lanping Chen, Lijun Zhao, Leisheng Liu, Yiqing Zhu, Junxue Liu, Yiman Xie, Yixin Yu, Yizhou Wang, Hansong Yuan, Jiajun Ge, Tong Zhao, Liebin Liu, Xiaoqing Zhang, Yuqi |
author_facet | Hong, Wenjing Sheng, Qiuyang Dong, Bin Wu, Lanping Chen, Lijun Zhao, Leisheng Liu, Yiqing Zhu, Junxue Liu, Yiman Xie, Yixin Yu, Yizhou Wang, Hansong Yuan, Jiajun Ge, Tong Zhao, Liebin Liu, Xiaoqing Zhang, Yuqi |
author_sort | Hong, Wenjing |
collection | PubMed |
description | Secundum atrial septal defect (ASD) is one of the most common congenital heart diseases (CHDs). This study aims to evaluate the feasibility and accuracy of automatic detection of ASD in children based on color Doppler echocardiographic images using convolutional neural networks. In this study, we propose a fully automatic detection system for ASD, which includes three stages. The first stage is used to identify four target echocardiographic views (that is, the subcostal view focusing on the atrium septum, the apical four-chamber view, the low parasternal four-chamber view, and the parasternal short-axis view). These four echocardiographic views are most useful for the diagnosis of ASD clinically. The second stage aims to segment the target cardiac structure and detect candidates for ASD. The third stage is to infer the final detection by utilizing the segmentation and detection results of the second stage. The proposed ASD detection system was developed and validated using a training set of 4,031 cases containing 370,057 echocardiographic images and an independent test set of 229 cases containing 203,619 images, of which 105 cases with ASD and 124 cases with intact atrial septum. Experimental results showed that the proposed ASD detection system achieved accuracy, recall, precision, specificity, and F1 score of 0.8833, 0.8545, 0.8577, 0.9136, and 0.8546, respectively on the image-level averages of the four most clinically useful echocardiographic views. The proposed system can automatically and accurately identify ASD, laying a good foundation for the subsequent artificial intelligence diagnosis of CHDs. |
format | Online Article Text |
id | pubmed-9019069 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-90190692022-04-21 Automatic Detection of Secundum Atrial Septal Defect in Children Based on Color Doppler Echocardiographic Images Using Convolutional Neural Networks Hong, Wenjing Sheng, Qiuyang Dong, Bin Wu, Lanping Chen, Lijun Zhao, Leisheng Liu, Yiqing Zhu, Junxue Liu, Yiman Xie, Yixin Yu, Yizhou Wang, Hansong Yuan, Jiajun Ge, Tong Zhao, Liebin Liu, Xiaoqing Zhang, Yuqi Front Cardiovasc Med Cardiovascular Medicine Secundum atrial septal defect (ASD) is one of the most common congenital heart diseases (CHDs). This study aims to evaluate the feasibility and accuracy of automatic detection of ASD in children based on color Doppler echocardiographic images using convolutional neural networks. In this study, we propose a fully automatic detection system for ASD, which includes three stages. The first stage is used to identify four target echocardiographic views (that is, the subcostal view focusing on the atrium septum, the apical four-chamber view, the low parasternal four-chamber view, and the parasternal short-axis view). These four echocardiographic views are most useful for the diagnosis of ASD clinically. The second stage aims to segment the target cardiac structure and detect candidates for ASD. The third stage is to infer the final detection by utilizing the segmentation and detection results of the second stage. The proposed ASD detection system was developed and validated using a training set of 4,031 cases containing 370,057 echocardiographic images and an independent test set of 229 cases containing 203,619 images, of which 105 cases with ASD and 124 cases with intact atrial septum. Experimental results showed that the proposed ASD detection system achieved accuracy, recall, precision, specificity, and F1 score of 0.8833, 0.8545, 0.8577, 0.9136, and 0.8546, respectively on the image-level averages of the four most clinically useful echocardiographic views. The proposed system can automatically and accurately identify ASD, laying a good foundation for the subsequent artificial intelligence diagnosis of CHDs. Frontiers Media S.A. 2022-04-06 /pmc/articles/PMC9019069/ /pubmed/35463790 http://dx.doi.org/10.3389/fcvm.2022.834285 Text en Copyright © 2022 Hong, Sheng, Dong, Wu, Chen, Zhao, Liu, Zhu, Liu, Xie, Yu, Wang, Yuan, Ge, Zhao, Liu 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 | Cardiovascular Medicine Hong, Wenjing Sheng, Qiuyang Dong, Bin Wu, Lanping Chen, Lijun Zhao, Leisheng Liu, Yiqing Zhu, Junxue Liu, Yiman Xie, Yixin Yu, Yizhou Wang, Hansong Yuan, Jiajun Ge, Tong Zhao, Liebin Liu, Xiaoqing Zhang, Yuqi Automatic Detection of Secundum Atrial Septal Defect in Children Based on Color Doppler Echocardiographic Images Using Convolutional Neural Networks |
title | Automatic Detection of Secundum Atrial Septal Defect in Children Based on Color Doppler Echocardiographic Images Using Convolutional Neural Networks |
title_full | Automatic Detection of Secundum Atrial Septal Defect in Children Based on Color Doppler Echocardiographic Images Using Convolutional Neural Networks |
title_fullStr | Automatic Detection of Secundum Atrial Septal Defect in Children Based on Color Doppler Echocardiographic Images Using Convolutional Neural Networks |
title_full_unstemmed | Automatic Detection of Secundum Atrial Septal Defect in Children Based on Color Doppler Echocardiographic Images Using Convolutional Neural Networks |
title_short | Automatic Detection of Secundum Atrial Septal Defect in Children Based on Color Doppler Echocardiographic Images Using Convolutional Neural Networks |
title_sort | automatic detection of secundum atrial septal defect in children based on color doppler echocardiographic images using convolutional neural networks |
topic | Cardiovascular Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9019069/ https://www.ncbi.nlm.nih.gov/pubmed/35463790 http://dx.doi.org/10.3389/fcvm.2022.834285 |
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