Automated and accurate view classification of fetal echocardiography using convolutional neural networks
CNNs have emerged as a promising tool for facilitating accurate and efficient view classification of fetal echocardiograms. This study paves the way for the future utilization of artificial intelligence (AI) to automate fetal heart screening and potentially support the diagnosis of CHD.
Authors
- Mingxing Xie
- Yali Yang (ORCID: https://orcid.org/0000-0002-6778-3869)
- Zhen Wang (ORCID: https://orcid.org/0000-0002-0978-4302)
- Jing Ma (ORCID: https://orcid.org/0000-0002-7464-8331)
- Sailing He (ORCID: https://orcid.org/0000-0002-3401-1125)
- Qin Hou (ORCID: https://orcid.org/0000-0002-3216-5788)
- Liu Hong (ORCID: https://orcid.org/0000-0003-0499-5618)
- Liu Juan-juan (ORCID: https://orcid.org/0000-0003-3107-5024)
- Cui Li (ORCID: https://orcid.org/0000-0003-0118-7375)
- Li Zhang (ORCID: https://orcid.org/0000-0002-9321-3421)
- Yi Zhang (ORCID: https://orcid.org/0000-0003-3929-3595)
- Tianshu Liu (ORCID: https://orcid.org/0000-0003-0085-4121)
- Jing Wang (ORCID: https://orcid.org/0000-0003-4955-5819)
- Jiawei Shi (ORCID: https://orcid.org/0009-0007-8850-9926)
- Ying Bai
- Wenhui Deng
- Haiyan Cao
- S. P. Xiao
Institutions
- Union Hospital (HK)
- State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing (CN)
- Huazhong University of Science and Technology (CN)
Publication Details
- Journal
- Quantitative Imaging in Medicine and Surgery
- Published
- 2025-11-21
- DOI
- https://doi.org/10.21037/qims-2025-556
- Primary Topic
- Fetal and Pediatric Neurological Disorders
- Type
- article
- Field-Weighted Citation Impact
- 0.00