Scientific Reports (Feb 2021)
Assisting scalable diagnosis automatically via CT images in the combat against COVID-19
- 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
Affiliations
- Bohan Liu
- Key Laboratory of Ministry of Industry and Information, Technology of Biomedical Engineering and Translational Medicine, Chinese PLA General Hospital
- Pan Liu
- Key Laboratory of Ministry of Industry and Information, Technology of Biomedical Engineering and Translational Medicine, Chinese PLA General Hospital
- Lutao Dai
- HKU Business School, The University of Hong Kong
- Yanlin Yang
- Department of Critical Care Medicine, Beijing Tiantan Hospital, Capital Medical University
- Peng Xie
- Department of Medical Imaging, Suizhou Hospital, Hubei University of Medicine (Suizhou Central Hospital)
- Yiqing Tan
- Department of Radiology, Wuhan Third Hospital, Tongren Hospital of Wuhan University
- Jicheng Du
- Department of Radiology, WenZhou Central Hospital
- Wei Shan
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University
- Chenghui Zhao
- Key Laboratory of Ministry of Industry and Information, Technology of Biomedical Engineering and Translational Medicine, Chinese PLA General Hospital
- Qin Zhong
- Key Laboratory of Ministry of Industry and Information, Technology of Biomedical Engineering and Translational Medicine, Chinese PLA General Hospital
- Xixiang Lin
- Key Laboratory of Ministry of Industry and Information, Technology of Biomedical Engineering and Translational Medicine, Chinese PLA General Hospital
- Xizhou Guan
- Pulmonary and Critical Care Medicine, Chinese PLA General Hospital
- Ning Xing
- Department of Radiology, Chinese PLA General Hospital
- Yuhui Sun
- Key Laboratory of Ministry of Industry and Information, Technology of Biomedical Engineering and Translational Medicine, Chinese PLA General Hospital
- Wenjun Wang
- Key Laboratory of Ministry of Industry and Information, Technology of Biomedical Engineering and Translational Medicine, Chinese PLA General Hospital
- Zhibing Zhang
- Department of Radiology, Xiantao First People’s Hospital, Affiliated to Yangtze University
- Xia Fu
- Department of Radiology, The First People’s Hospital of Jiangxia District
- Yanqing Fan
- Department of Radiology, Wuhan Jinyintan Hospital
- Meifang Li
- Department of Medical Imaging, Affiliated Hospital of Putian University
- Na Zhang
- Department of Radiology, Chengdu Public Health Clinical Medical Center
- Lin Li
- Department of Radiology, Wuhan Huangpi People’s Hospital
- Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University
- Lin Xu
- Department of Medical Imaging Center, Dazhou Central Hospital
- Jingbo Du
- Department of Radiology, Beijing Daxing District People’s Hospital (Capital Medical University Daxing Teaching Hospital)
- Zhenhua Zhao
- Department of Radiology, Shaoxing People’s Hospital (The First Affiliated Hospital of Shaoxing University)
- Xuelong Hu
- Department of Radiology, The People’s Hospital of Zigui
- Weipeng Fan
- Department of Medical Imaging, Anshan Central Hospital
- Rongpin Wang
- Department of Medical Imaging, Guizhou Provincial People’s Hospital
- Chongchong Wu
- Department of Radiology, Chinese PLA General Hospital
- Yongkang Nie
- Department of Radiology, Chinese PLA General Hospital
- Liuquan Cheng
- Department of Radiology, Chinese PLA General Hospital
- Lin Ma
- Department of Radiology, Chinese PLA General Hospital
- Zongren Li
- Key Laboratory of Ministry of Industry and Information, Technology of Biomedical Engineering and Translational Medicine, Chinese PLA General Hospital
- Qian Jia
- Key Laboratory of Ministry of Industry and Information, Technology of Biomedical Engineering and Translational Medicine, Chinese PLA General Hospital
- Minchao Liu
- Department of Computer Application and Management, Chinese PLA General Hospital
- Huayuan Guo
- Department of Computer Application and Management, Chinese PLA General Hospital
- Gao Huang
- Department of Automation, Tsinghua University
- Haipeng Shen
- HKU Business School, The University of Hong Kong
- Liang Zhang
- Biomind Technology Co. Ltd
- Peifang Zhang
- Biomind Technology Co. Ltd
- Gang Guo
- Biomind Technology Co. Ltd
- Hao Li
- China National Clinical Research Center for Neurological Diseases, Center for Bigdata Analytics and Artificial Intelligence
- Weimin An
- Department of Radiology, 5th Medical Center, Chinese PLA General Hospital
- Jianxin Zhou
- Department of Critical Care Medicine, Beijing Tiantan Hospital, Capital Medical University
- Kunlun He
- Key Laboratory of Ministry of Industry and Information, Technology of Biomedical Engineering and Translational Medicine, Chinese PLA General Hospital
- DOI
- https://doi.org/10.1038/s41598-021-83424-5
- Journal volume & issue
-
Vol. 11,
no. 1
pp. 1 – 8
Abstract
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.
