A multicenter study on two-stage transfer learning model for duct-dependent CHDs screening in fetal echocardiography

作者：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^*

来源：npj Digital Medicine, 2023, 6(1): 143.

DOI：10.1038/s41746-023-00883-y

• PRENATAL-DIAGNOSIS
• MANAGEMENT
• LIFE
• COARCTATION
• STATEMENT
• SURGERY
• DISEASE
• TRENDS

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.

• 单位
  武汉大学; 广州医学院; 广东省心血管病研究所; 广东省人民医院