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
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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“CHD was effectively detected by AI models generated by the research groups of Gong et al. and Nurmaini et al. [61,62], and selectively for the diagnosis of total anomalous pulmonary venous connection by Wang et al. [63].”
In the citing paper
“First, most of the studies only used 4CV images as the input for their algorithms [55,57,61,62,63,67,70]”
In the citing paper
“Advantages are the facilitation of standard view acquisition [58,60] and CHD detection [53,61,62], as well as a significant reduction in examination time [52,55].”
Citations
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Items 101–103 of 103
Latent Motion Spectral Regularity for Congenital Heart Defect Screening in Fetal Echocardiography
Yang, Y., Yang, Q., Peng, C., Šalović, B., D’Alberti, E., Carvalho, J., Patey, O. V., Papageorghiou, A. T., & Noble, J. A. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38098-2_34
Fibers of Asymmetric Similarity: A Framework for Clinical and Imaging Data
Müller, J. P., Baugh, M., Wright, R., Day, T. G., Razavi, R., & Kainz, B. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38091-3_29
A Novel Ensemble Model for Heart Disease Diagnosis using ECG Image Classification
Duggirala, S. B., Mishra, R. K., & Chowdary, S. (2026) · International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) · doi:10.1109/i-smac70384.2026.11747461
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)
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2020-04
- Volume
- 39
- Issue
- 4
- Pages
- 1206-1222
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- 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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