Intelligent auxiliary diagnosis of atrial septal defect based on view classification

ZHANG Wen-jing; LI Wen-xiu; LIU Ai-jun; WU Xing-kun; LI Jian-feng; LUO Tao

doi:10.13374/j.issn2095-9389.2021.01.14.007

Volume 43 Issue 9

Sep. 2021

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Article Navigation > Chinese Journal of Engineering > 2021 > 43(9): 1166-1173

ZHANG Wen-jing, LI Wen-xiu, LIU Ai-jun, WU Xing-kun, LI Jian-feng, LUO Tao. Intelligent auxiliary diagnosis of atrial septal defect based on view classification[J]. Chinese Journal of Engineering, 2021, 43(9): 1166-1173. doi: 10.13374/j.issn2095-9389.2021.01.14.007

Citation:

ZHANG Wen-jing, LI Wen-xiu, LIU Ai-jun, WU Xing-kun, LI Jian-feng, LUO Tao. Intelligent auxiliary diagnosis of atrial septal defect based on view classification[J]. Chinese Journal of Engineering, 2021, 43(9): 1166-1173. doi: 10.13374/j.issn2095-9389.2021.01.14.007

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Intelligent auxiliary diagnosis of atrial septal defect based on view classification

doi: 10.13374/j.issn2095-9389.2021.01.14.007

1.
Beijing Laboratory of Advanced Information Networks, Beijing University of Posts and Telecommunication, Beijing 100876, China
2.
Pediatric Cardiovascular Center, Beijing Anzhen Hospital Affiliated to Capital Medical University, Beijing 100029, China
3.
Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunication, Beijing 100876, China

More Information

Corresponding author: E-mail: tluo@bupt.edu.cn
Received Date: 2021-01-14
Available Online: 2021-04-07
Publish Date: 2021-09-18

Abstract

Abstract

Atrial septal defect (ASD) is common congenital heart disease. The detection rate of congenital heart disease has increased year by year, and ASD accounted for the largest proportion of it, reaching 37.31%. The ASD patient will suffer from shortness of breath, palpitation, weakness, etc., with symptoms worsening with advanced age. The ASD patient will not suffer from congenital heart disease if their condition is diagnosed early. Echocardiography is a powerful and cost-effective means of detecting ASD. However, the disadvantages of echocardiography, such as noise and poor imaging quality, cause misdiagnosis of ASD. Hence, research into echocardiography-based efficient and effective detection of ASD with a deep neural network is of great significance. For echocardiography is noisy and fuzzy, and the learning and feature expression ability of the traditional convolutional neural network architecture is limited, a feature view classification based atrial septal defect intelligent auxiliary diagnostic model architecture was proposed. The different views of echocardiography possess different features, demanding more precise model extraction and combined features from echocardiography. The proposed model architecture integrates the semantic characteristics of several views, significantly improving the accuracy of diagnosis. In addition, with the aim of denoising and preserving edges, a bilateral filtering algorithm was performed. Furthermore, an ASD intelligent auxiliary diagnostic system was built based on the proposed model. The results show that the accuracy of the ASD auxiliary diagnostic system reaches 97.8%, and the false-negative rate is greatly reduced compared with the traditional convolutional neural network architecture.
- deep learning,
- echocardiography,
- atrial septal defect,
- view classification,
- bilateral filtering

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References(25)

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