Standard Echocardiographic View Recognition in Diagnosis of Congenital Heart Defects in Children Using Deep Learning Based on Knowledge Distillation

Frontiers in Pediatrics

Kunlun Gao¹  Xiaoqing Liu¹  Qiuyang Sheng¹  Yizhou Yu¹  Lanping Wu²  Wenjing Hong²  Lijun Chen²  Yuqi Zhang²  Liebin Zhao³  Bin Dong³ 

[1] Deepwise Artificial Intelligence Laboratory, Beijing, China;Department of Pediatric Cardiology, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China;Shanghai Engineering Research Center of Intelligence Pediatrics, Shanghai, China;

关键词: standard echocardiographic view;    congenital heart defect;    deep learning;    convolutional neural network;    knowledge distillation;   

DOI  :  10.3389/fped.2021.770182

来源: DOAJ

【 摘 要 】

Standard echocardiographic view recognition is a prerequisite for automatic diagnosis of congenital heart defects (CHDs). This study aims to evaluate the feasibility and accuracy of standard echocardiographic view recognition in the diagnosis of CHDs in children using convolutional neural networks (CNNs). A new deep learning-based neural network method was proposed to automatically and efficiently identify commonly used standard echocardiographic views. A total of 367,571 echocardiographic image slices from 3,772 subjects were used to train and validate the proposed echocardiographic view recognition model where 23 standard echocardiographic views commonly used to diagnose CHDs in children were identified. The F1 scores of a majority of views were all ≥0.90, including subcostal sagittal/coronal view of the atrium septum, apical four-chamber view, apical five-chamber view, low parasternal four-chamber view, sax-mid, sax-basal, parasternal long-axis view of the left ventricle (PSLV), suprasternal long-axis view of the entire aortic arch, M-mode echocardiographic recording of the aortic (M-AO) and the left ventricle at the level of the papillary muscle (M-LV), Doppler recording from the mitral valve (DP-MV), the tricuspid valve (DP-TV), the ascending aorta (DP-AAO), the pulmonary valve (DP-PV), and the descending aorta (DP-DAO). This study provides a solid foundation for the subsequent use of artificial intelligence (AI) to identify CHDs in children.

【 授权许可】

Unknown