Your browser doesn't support javascript.
loading
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; Weimin An; Hao Li; Jianxin Zhou; Kunlun He.
Afiliação
  • Bohan Liu; 1.Key Laboratory of Ministry of Industry and Information Technology of Biomedical Engineering and Translational Medicine, Chinese PLA General Hospital; 2.Transl
  • Pan Liu; 1.Key Laboratory of Ministry of Industry and Information Technology of Biomedical Engineering and Translational Medicine; 2.Translational Medical Research Cente
  • Lutao Dai; Faculty of Business and Economics, The University of Hong Kong
  • Yanlin Yang; Department of Critical Care Medicine, Beijing Tiantan Hospital
  • 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; 1.Key Laboratory of Ministry of Industry and Information Technology of Biomedical Engineering and Translational Medicine, Chinese PLA General Hospital; 2.Transl
  • Qin Zhong; 1.Key Laboratory of Ministry of Industry and Information Technology of Biomedical Engineering and Translational Medicine, Chinese PLA General Hospital; 2.Transl
  • Xixiang Lin; 1.Key Laboratory of Ministry of Industry and Information Technology of Biomedical Engineering and Translational Medicine, Chinese PLA General Hospital; 2.Transl
  • Xizhou Guan; Pulmonary and Critical Care Medicine, Chinese PLA General Hospital
  • Ning Xing; Department of Radiology, Chinese PLA General Hospital
  • Yuhui Sun; 1.Key Laboratory of Ministry of Industry and Information Technology of Biomedical Engineering and Translational Medicine, Chinese PLA General Hospital; 2.Transl
  • Wenjun Wang; 1.Key Laboratory of Ministry of Industry and Information Technology of Biomedical Engineering and Translational Medicine, Chinese PLA General Hospital; 2.Transl
  • 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; 1.Department of Radiology, Wuhan Huangpi People's Hospital; 2.Jianghan University Affiliated 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; 1.Key Laboratory of Ministry of Industry and Information Technology of Biomedical Engineering and Translational Medicine, Chinese PLA General Hospital; 2.Transl
  • Qian Jia; 1.Key Laboratory of Ministry of Industry and Information Technology of Biomedical Engineering and Translational Medicine, Chinese PLA General Hospital; 2.Transl
  • 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; 1.Faculty of Business and Economics, The University of Hong Kong; 2.China National Clinical Research Center for Neurological Diseases, Center for Bigdata Analyt
  • Weimin An; Department of Radiology, 5th Medical Center, Chinese PLA General Hospital
  • Hao Li; China National Clinical Research Center for Neurological Diseases, Center for Bigdata Analytics and Artificial Intelligence
  • Jianxin Zhou; Department of Critical Care Medicine, Beijing Tiantan Hospital, Capital Medical University
  • Kunlun He; 1.Key Laboratory of Ministry of Industry and Information Technology of Biomedical Engineering and Translational Medicine, Chinese PLA General Hospital; 2.Transl
Preprint em Inglês | medRxiv | ID: ppmedrxiv-20093732
ABSTRACT
Introductory paragraphThe pandemic of coronavirus Disease 2019 (COVID-19) caused enormous loss of life globally. 1-3 Case identification is critical. The reference method is using real-time reverse transcription PCR (rRT-PCR) assays, with limitations that 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 application of deep learning (DL) to the 3D CT images could help identify COVID-19 infections. Using the 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 patients. COVIDNet achieved an accuracy rate of 94.3% and an area under the curve (AUC) of 0.98. Application of DL to CT images may improve both the efficiency and capacity of case detection and long-term surveillance.
Licença
cc_no
Texto completo: Disponível Coleções: Preprints Base de dados: medRxiv Idioma: Inglês Ano de publicação: 2020 Tipo de documento: Preprint
Texto completo: Disponível Coleções: Preprints Base de dados: medRxiv Idioma: Inglês Ano de publicação: 2020 Tipo de documento: Preprint
...