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- Title
A multicenter study on two-stage transfer learning model for duct-dependent CHDs screening in fetal echocardiography.
- Authors
Tang, Jiajie; Liang, Yongen; Jiang, Yuxuan; Liu, Jinrong; Zhang, Rui; Huang, Danping; Pang, Chengcheng; Huang, Chen; Luo, Dongni; Zhou, Xue; Li, Ruizhuo; Zhang, Kanghui; Xie, Bingbing; Hu, Lianting; Zhu, Fanfan; Xia, Huimin; Lu, Long; Wang, Hongying
- Abstract
Duct-dependent congenital heart diseases (CHDs) are a serious form of CHD with a low detection rate, especially in underdeveloped countries and areas. Although existing studies have developed models for fetal heart structure identification, there is a lack of comprehensive evaluation of the long axis of the aorta. In this study, a total of 6698 images and 48 videos are collected to develop and test a two-stage deep transfer learning model named DDCHD-DenseNet for screening critical duct-dependent CHDs. The model achieves a sensitivity of 0.973, 0.843, 0.769, and 0.759, and a specificity of 0.985, 0.967, 0.956, and 0.759, respectively, on the four multicenter test sets. It is expected to be employed as a potential automatic screening tool for hierarchical care and computer-aided diagnosis. Our two-stage strategy effectively improves the robustness of the model and can be extended to screen for other fetal heart development defects.
- Subjects
CHINA; FETAL echocardiography; DEEP learning; RESEARCH; PATENT ductus arteriosus; CONGENITAL heart disease; MEDICAL screening; LEARNING strategies; RESEARCH funding; COMPUTER-aided diagnosis; SENSITIVITY &; specificity (Statistics); COMPUTER-assisted image analysis (Medicine); FETAL abnormalities
- Publication
NPJ Digital Medicine, 2023, Vol 6, Issue 1, p1
- ISSN
2398-6352
- Publication type
Article
- DOI
10.1038/s41746-023-00883-y