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Cited by 103Congenital Heart Disease StudiesArtificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

The DOI matches its Crossref record; 42 of the 45 references it cites were found in a registry or an index.

Fetal Congenital Heart Disease Echocardiogram Screening Based on DGACNN: Adversarial One-Class Classification Combined with Video Transfer Learning

Gong, Yuxin, Zhang, Yingying, Zhu, Haogang, Lv, Jing, Cheng, Qian, Zhang, Hongjia, He, Yihua, Wang, Shuliang·2020·IEEE Transactions on Medical Imaging
DOI 10.1109/tmi.2019.2946059
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Abstract

Fetal congenital heart disease (FHD) is a common and serious congenital malformation in children. In Asia, FHD birth defect rates have reached as high as 9.3%. For the early detection of birth defects and mortality, echocardiography remains the most effective method for screening fetal heart malformations. However, standard echocardiograms of the fetal heart, especially four-chamber view images, are difficult to obtain. In addition, the pathophysiological changes in fetal hearts during different pregnancy periods lead to ever-changing two-dimensional fetal heart structures and hemodynamics, and it requires extensive professional knowledge to recognize and judge disease development. Thus, research on the automatic screening for FHD is necessary. In this paper, we proposed a new model named DGACNN that shows the best performance in recognizing FHD, achieving a rate of 85%. The motivation for this network is to deal with the problem that there are insufficient training datasets to train a robust model. There are many unlabeled video slices, but they are tough and time-consuming to annotate. Thus, how to use these un-annotated video slices to improve the DGACNN capability for recognizing FHD, in terms of both recognition accuracy and robustness, is very meaningful for FHD screening. The architecture of DGACNN comprises two parts, that is, DANomaly and GACNN (Wgan-GP and CNN). DANomaly, similar to the ALOCC network, but incorporates cycle adversarial learning to train an end-to-end one-class classification (OCC) network that is more robust and has a higher accuracy than ALOCC in screening video slices. For the GACNN architecture, we use FCH (four chamber heart) video slices at around the end-systole, as screened by DANomaly, to train a WGAN-GP for the purpose of obtaining ideal low-level features that can robustly improve the FHD recognition accuracy. A few annotated video slices, as screened by DANomaly, can also be used for data augmentation so as to improve the FHD recognition further. The experiments show that the DGACNN outperforms other state-of-the-art networks by 1%-20% in recognizing FHD. A comparison experiment shows that the proposed network already outperforms the performance of expert cardiologists in recognizing FHD, reaching 84% in a test. Thus, the proposed architecture has high potential for helping cardiologists complete early FHD screenings.

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Metadata

Authors
Gong, Y., Zhang, Y., Zhu, H., Lv, J., Cheng, Q., Zhang, H., He, Y., & Wang, S.
Affiliations
  • State Key Laboratory of Software Development Environment (CN)
  • Capital Medical University (CN)
  • Beijing Institute of Technology (CN)
ORCID
Venue
IEEE Transactions on Medical Imaging
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2020-04
Volume
39
Issue
4
Pages
1206-1222
ISSN
  • 0278-0062 (ISSN-L)
  • 1558-254X
DOI
10.1109/tmi.2019.2946059
OpenAlex
W2979616729
Cited by
101
References
45
Citations by year
2026: 17 · 2025: 18 · 2024: 20 · 2023: 14 · 2022: 18 · 2021: 12 · 2020: 2
FWCI
6.09
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • IEEE Transactions on Medical Imaging (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • State Key Laboratory of Software Development Environment
  • Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, China
  • Beihang University
  • National Natural Science Foundation of China (61702027)
  • Beijing Science and Technology Planning Project (Z171100000117022, Z181100001918008)
  • Beijing Municipal Science and Technology Commission (PXM2018014226000013)
Topics
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Congenital Heart Disease Studies
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
fetal congenital heart disease, fetal echocardiography, automated screening, one-class classification, adversarial learning, WGAN-GP, transfer learning, data augmentation, limited labeled data
MeSH terms
Deep Learning, Algorithms, Echocardiography, Female, Heart Defects, Congenital, Humans, Image Interpretation, Computer-Assisted, Pregnancy, Video Recording, Ultrasonography, Prenatal

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