Assisting scalable diagnosis automatically via CT images in the combat against COVID-19

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 mani...

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Autores principales:	Bohan Liu, Pan Liu, Lutao Dai, Yanlin Yang, Peng Xie, Yiqing Tan, Jicheng Du, Wei Shan, Chenghui Zhao, Qin Zhong, Xixiang Lin, Xizhou Guan, Ning Xing, Yuhui Sun, Wenjun Wang, Zhibing Zhang, Xia Fu, Yanqing Fan, Meifang Li, Na Zhang, Lin Li, Yaou Liu, Lin Xu, Jingbo Du, Zhenhua Zhao, Xuelong Hu, Weipeng Fan, Rongpin Wang, Chongchong Wu, Yongkang Nie, Liuquan Cheng, Lin Ma, Zongren Li, Qian Jia, Minchao Liu, Huayuan Guo, Gao Huang, Haipeng Shen, Liang Zhang, Peifang Zhang, Gang Guo, Hao Li, Weimin An, Jianxin Zhou, Kunlun He
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0809c4d7677844d1b2bbdca646352a4e
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spelling	oai:doaj.org-article:0809c4d7677844d1b2bbdca646352a4e2021-12-02T14:21:58ZAssisting scalable diagnosis automatically via CT images in the combat against COVID-1910.1038/s41598-021-83424-52045-2322https://doaj.org/article/0809c4d7677844d1b2bbdca646352a4e2021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-83424-5https://doaj.org/toc/2045-2322Abstract 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.Bohan LiuPan LiuLutao DaiYanlin YangPeng XieYiqing TanJicheng DuWei ShanChenghui ZhaoQin ZhongXixiang LinXizhou GuanNing XingYuhui SunWenjun WangZhibing ZhangXia FuYanqing FanMeifang LiNa ZhangLin LiYaou LiuLin XuJingbo DuZhenhua ZhaoXuelong HuWeipeng FanRongpin WangChongchong WuYongkang NieLiuquan ChengLin MaZongren LiQian JiaMinchao LiuHuayuan GuoGao HuangHaipeng ShenLiang ZhangPeifang ZhangGang GuoHao LiWeimin AnJianxin ZhouKunlun HeNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Bohan Liu Pan Liu Lutao Dai Yanlin Yang Peng Xie Yiqing Tan Jicheng Du Wei Shan Chenghui Zhao Qin Zhong Xixiang Lin Xizhou Guan Ning Xing Yuhui Sun Wenjun Wang Zhibing Zhang Xia Fu Yanqing Fan Meifang Li Na Zhang Lin Li Yaou Liu Lin Xu Jingbo Du Zhenhua Zhao Xuelong Hu Weipeng Fan Rongpin Wang Chongchong Wu Yongkang Nie Liuquan Cheng Lin Ma Zongren Li Qian Jia Minchao Liu Huayuan Guo Gao Huang Haipeng Shen Liang Zhang Peifang Zhang Gang Guo Hao Li Weimin An Jianxin Zhou Kunlun He Assisting scalable diagnosis automatically via CT images in the combat against COVID-19
description	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.
format	article
author	Bohan Liu Pan Liu Lutao Dai Yanlin Yang Peng Xie Yiqing Tan Jicheng Du Wei Shan Chenghui Zhao Qin Zhong Xixiang Lin Xizhou Guan Ning Xing Yuhui Sun Wenjun Wang Zhibing Zhang Xia Fu Yanqing Fan Meifang Li Na Zhang Lin Li Yaou Liu Lin Xu Jingbo Du Zhenhua Zhao Xuelong Hu Weipeng Fan Rongpin Wang Chongchong Wu Yongkang Nie Liuquan Cheng Lin Ma Zongren Li Qian Jia Minchao Liu Huayuan Guo Gao Huang Haipeng Shen Liang Zhang Peifang Zhang Gang Guo Hao Li Weimin An Jianxin Zhou Kunlun He
author_facet	Bohan Liu Pan Liu Lutao Dai Yanlin Yang Peng Xie Yiqing Tan Jicheng Du Wei Shan Chenghui Zhao Qin Zhong Xixiang Lin Xizhou Guan Ning Xing Yuhui Sun Wenjun Wang Zhibing Zhang Xia Fu Yanqing Fan Meifang Li Na Zhang Lin Li Yaou Liu Lin Xu Jingbo Du Zhenhua Zhao Xuelong Hu Weipeng Fan Rongpin Wang Chongchong Wu Yongkang Nie Liuquan Cheng Lin Ma Zongren Li Qian Jia Minchao Liu Huayuan Guo Gao Huang Haipeng Shen Liang Zhang Peifang Zhang Gang Guo Hao Li Weimin An Jianxin Zhou Kunlun He
author_sort	Bohan Liu
title	Assisting scalable diagnosis automatically via CT images in the combat against COVID-19
title_short	Assisting scalable diagnosis automatically via CT images in the combat against COVID-19
title_full	Assisting scalable diagnosis automatically via CT images in the combat against COVID-19
title_fullStr	Assisting scalable diagnosis automatically via CT images in the combat against COVID-19
title_full_unstemmed	Assisting scalable diagnosis automatically via CT images in the combat against COVID-19
title_sort	assisting scalable diagnosis automatically via ct images in the combat against covid-19
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/0809c4d7677844d1b2bbdca646352a4e
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Assisting scalable diagnosis automatically via CT images in the combat against COVID-19

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