| Literature DB >> 33603047 |
Bohan Liu1,2, Pan Liu1,2, Lutao Dai3, Yanlin Yang4, Peng Xie5, Yiqing Tan6, Jicheng Du7, Wei Shan8, Chenghui Zhao1,2, Qin Zhong1,2, Xixiang Lin1,2, Xizhou Guan9, Ning Xing10, Yuhui Sun1,2, Wenjun Wang1,2, Zhibing Zhang11, Xia Fu12, Yanqing Fan13, Meifang Li14, Na Zhang15, Lin Li16,17, Yaou Liu18, Lin Xu19, Jingbo Du20, Zhenhua Zhao21, Xuelong Hu22, Weipeng Fan23, Rongpin Wang24, Chongchong Wu10, Yongkang Nie10, Liuquan Cheng10, Lin Ma10, Zongren Li1,2, Qian Jia1,2, Minchao Liu25, Huayuan Guo25, Gao Huang26, Haipeng Shen3,27, Liang Zhang28, Peifang Zhang28, Gang Guo28, Hao Li27, Weimin An29, Jianxin Zhou30, Kunlun He31,32.
Abstract
The pandemic of Coronavirus Disease 2019 (COVID-19) is causing enormous loss of life globally. Prompt case identification is critical. The reference method is the real-time reverse transcription PCR (RT-PCR) assay, whose limitations may curb its prompt large-scale application. COVID-19 manifests with chest computed tomography (CT) abnormalities, some even before the onset of symptoms. We tested the hypothesis that the application of deep learning (DL) to 3D CT images could help identify COVID-19 infections. Using data from 920 COVID-19 and 1,073 non-COVID-19 pneumonia patients, we developed a modified DenseNet-264 model, COVIDNet, to classify CT images to either class. When tested on an independent set of 233 COVID-19 and 289 non-COVID-19 pneumonia patients, COVIDNet achieved an accuracy rate of 94.3% and an area under the curve of 0.98. As of March 23, 2020, the COVIDNet system had been used 11,966 times with a sensitivity of 91.12% and a specificity of 88.50% in six hospitals with PCR confirmation. Application of DL to CT images may improve both efficiency and capacity of case detection and long-term surveillance.Entities:
Year: 2021 PMID： 33603047 PMCID： PMC7892869 DOI： 10.1038/s41598-021-83424-5
Source DB: PubMed Journal: Sci Rep ISSN： 2045-2322 Impact factor: 4.379