Evaluation of lung involvement in COVID-19 pneumonia based on ultrasound images

BioMedical Engineering OnLine

Jinhua Yu¹  Zhaoyu Hu¹  Zhenhua Liu²  Yijie Dong²  Jianqiao Zhou²  Jingjing Huang³  Hui Zhang³  Xia Shi³  Xujuan Pu³  Jianjian Liu³  Aihua Liu⁴  Bin Huang⁵  Yang Xiao⁶ 

[1] Department of Electronic Engineering, Fudan University;Department of Ultrasound, Ruijin Hospital, Shanghai Jiaotong University School of Medicine;Department of Ultrasound, Shanghai Public Health Clinical Center;Department of Ultrasound, The Six Hospital of Wuhan, Affiliated Hospital of Jianghang University;Department of Ultrasound, Xixi Hospital of Hangzhou;Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences;

关键词: Ultrasound;    Lung involvement;    Classification;    COVID-19;    Neural network;   

DOI  :  10.1186/s12938-021-00863-x

来源: DOAJ

【 摘 要 】

Abstract Background Lung ultrasound (LUS) can be an important imaging tool for the diagnosis and assessment of lung involvement. Ultrasound sonograms have been confirmed to illustrate damage to a person’s lungs, which means that the correct classification and scoring of a patient’s sonogram can be used to assess lung involvement. Methods The purpose of this study was to establish a lung involvement assessment model based on deep learning. A novel multimodal channel and receptive field attention network combined with ResNeXt (MCRFNet) was proposed to classify sonograms, and the network can automatically fuse shallow features and determine the importance of different channels and respective fields. Finally, sonogram classes were transformed into scores to evaluate lung involvement from the initial diagnosis to rehabilitation. Results and conclusion Using multicenter and multimodal ultrasound data from 104 patients, the diagnostic model achieved 94.39% accuracy, 82.28% precision, 76.27% sensitivity, and 96.44% specificity. The lung involvement severity and the trend of COVID-19 pneumonia were evaluated quantitatively.

【 授权许可】

Unknown