A multicenter study on two-stage transfer learning model for duct-dependent CHDs screening in fetal echocardiography
Jiajie Tang, Yongen Liang, Yuxuan Jiang, Jinrong Liu, Rui Zhang, Danping Huang, Chengcheng Pang, Chen Huang, Dongni Luo, Xuedong Zhou, Ruizhuo Li, Kanghui Zhang, Bingbing Xie, Lianting Hu, Fanfan Zhu, Huimin Xia, Long Lu, Hongying Wang
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.