Research Coronavirus Disease 2019
A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia
a State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, China
b Department of Infectious Disease, Wenzhou Central Hospital, Wenzhou 325000, China
c Department of Infectious Disease, The First People's Hospital of Wenling, Wenling 317500, China
d Artificial Intelligence Lab, Hangzhou AiSmartVision Co., Ltd., Hangzhou 310012, China
e Department of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, China
f Department of Hepatobiliary and Pancreatic Surgery & Key Lab of Pancreatic Diseases Research of Zhejiang Province & The Innovation Centre for the Study of Pancreatic Diseases of Zhejiang Province & Clinical Medical Research Center of Hepatobiliary and Pancreatic Diseases in Zhejiang Province & Precision Innovation Center of the Diagnosis and Treatment of Hepatobiliary and Pancreatic Diseases of Zhejiang University, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, China
# These authors contributed equally to this work.
The real-time reverse transcription-polymerase chain reaction (RT-PCR) detection of viral RNA from sputum or nasopharyngeal swab had a relatively low positive rate in the early stage of coronavirus disease 2019 (COVID-19). Meanwhile, the manifestations of COVID-19 as seen through computed tomography (CT) imaging show individual characteristics that differ from those of other types of viral pneumonia such as influenza-A viral pneumonia (IAVP). This study aimed to establish an early screening model to distinguish COVID-19 pneumonia from IAVP and healthy cases through pulmonary CT images using deep learning techniques. A total of 618 CT samples were collected: 219 samples from 110 patients with COVID-19 (mean age 50 years; 63 (57.3%) male patients); 224 samples from 224 patients with IAVP (mean age 61 years; 156 (69.6%) male patients); and 175 samples from 175 healthy cases (mean age 39 years; 97 (55.4%) male patients). All CT samples were contributed from three COVID-19-designated hospitals in Zhejiang Province, China. First, the candidate infection regions were segmented out from the pulmonary CT image set using a 3D deep learning model. These separated images were then categorized into the COVID-19, IAVP, and irrelevant to infection (ITI) groups, together with the corresponding confidence scores, using a location-attention classification model. Finally, the infection type and overall confidence score for each CT case were calculated using the Noisy-OR Bayesian function. The experimental result of the benchmark dataset showed that the overall accuracy rate was 86.7% in terms of all the CT cases taken together. The deep learning models established in this study were effective for the early screening of COVID-19 patients and were demonstrated to be a promising supplementary diagnostic method for frontline clinical doctors.
[ 1 ] Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, et al. A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med 2020;382 (8):727–33. link1
[ 2 ] Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, et al. Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. N Engl J Med 2020;382(13):1199–207. link1
[ 3 ] Cohen J, Normile D. New SARS-like virus in China triggers alarm. Science 2020;367(6475):234–5. link1
[ 4 ] Corman VM, Landt O, Kaiser M, Molenkamp R, Meijer A, Chu DKW, et al. Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR. Euro Surveill 2020;25(3):23–30. link1
[ 5 ] Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 2020;395 (10223):497–506. link1
[ 6 ] Chan JFW, Yuan S, Kok KH, To KKW, Chu H, Yang J, et al. A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-toperson transmission: a study of a family cluster. Lancet 2020;395 (10223):514–23. link1
[ 7 ] National Health Commission of the People’s Republic of China, National Administration of Traditional Chinese Medicine. Diagnosis and treatment protocol for novel coronavirus pneumonia (trial version 7) [Internet]. Beijing: National Health Commission of the People’s Republic of China; [cited 2020 Mar 8]. Available from: http://www.nhc.gov.cn/yzygj/s7653p/202003/46c9294a 7dfe4cef80dc7f5912eb1989/files/ce3e6945832a438eaae415350a8ce964.pdf. Chinese. link1
[ 8 ] Loeffelholz MJ, Tang YW. Laboratory diagnosis of emerging human coronavirus infections—the state of the art. Emerg Microbes Infect 2020;9 (1):747–56. link1
[ 9 ] Wang W, Xu Y, Gao R, Lu R, Han K, Wu G, et al. Detection of SARS-CoV-2 in different types of clinical specimens. JAMA 2020;323(18):1843–4.
 Zhang W, Du R, Li B, Zheng X, Yang X, Hu B, et al. Molecular and serological investigation of 2019-nCoV infected patients: implication of multiple shedding routes. Emerg Microbes Infect 2020;9(1):386–9. link1
 Long Q, Deng H, Chen J, Hu J, Liu B, Liao P, et al. Antibody responses to SARSCoV-2 in COVID-19 patients: the perspective application of serological tests in clinical practice. 2020. medRxiv:2020.03.18.20038018.
 National Health Commission of the People’s Republic of China, National Administration of Traditional Chinese Medicine. Diagnosis and treatment protocol for novel coronavirus pneumonia (trial version 5) [Internet]. Beijing: National Health Commission of the People’s Republic of China; [cited 2020 Feb 5]. Available from: http://www.nhc.gov.cn/yzygj/s7653p/202002/ d4b895337e19445f8d728fcaf1e3e13a/files/ab6bec7f93e64e7f998d80299120 3cd6.pdf. Chinese. link1
 Liu X, Guo S, Yang B, Ma S, Zhang H, Li J, et al. Automatic organ segmentation for CT scans based on super-pixel and convolutional neural networks. J Digit Imaging 2018;31(5):748–60. link1
 Gharbi M, Chen J, Barron JT, Hasinoff SW, Durand F. Deep bilateral learning for real-time image enhancement. ACM Trans Graph 2017;36(4):118. link1
 Hesamian MH, Jia W, He X, Kennedy P. Deep learning techniques for medical image segmentation: achievements and challenges. J Digit Imaging 2019;32 (4):582–96. link1
 Akagi M, Nakamura Y, Higaki T, Narita K, Honda Y, Zhou J, et al. Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT. Eur Radiol 2019;29(11):6163–71. link1
 Nardelli P, Jimenez-Carretero D, Bermejo-Pelaez D, Washko GR, Rahaghi FN, Ledesma-Carbayo MJ, et al. Pulmonary artery-vein classification in CT images using deep learning. IEEE Trans Med Imaging 2018;37(11):2428–40. link1
 Zhu W, Huang Y, Zeng L, Chen X, Liu Y, Qian Z, et al. AnatomyNet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Med Phys 2019;46(2):576–89. link1
 Huang P, Park S, Yan R, Lee J, Chu LC, Lin CT, et al. Added value of computeraided CT image features for early lung cancer diagnosis with small pulmonary nodules: amatched case-control study. Radiology 2018;286(1):286–95. link1
 Ardila D, Kiraly AP, Bharadwaj S, Choi B, Reicher JJ, Peng L, et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med 2019;25(6):954–61. link1
 Esteva A, Kuprel B, NovoaR A, Ko J, Swetter SM, Blau HM, et al. Dermatologistlevel classification of skin cancer with deep neural networks. Nature 2017;542 (7639):115–8. link1
 Lakhani P, Sundaram B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 2017;284(2):574–82. link1
 Wu W, Li X, Du P, Lang G, Xu M, Xu K, et al. A deep learning system that generates quantitative CT reports for diagnosing pulmonary tuberculosis. 2019. arXiv:1910.02285v1.
 Li L, Huang H, Jin X. AE-CNN classification of pulmonary tuberculosis based on CT images. In: Proceedings of the 9th International Conference on Information Technology in Medicine and Education (ITME); 2018 Oct 19–21; Zhejiang, China. New York: IEEE; 2018.
 Onis´ko A, Druzdzel M, Wasyluk H. Learning Bayesian network parameters from small data sets: application of Noisy-OR gates. Int J Approx Reason 2001;27(2):165–82. link1
 Milletari F, Navab N, Ahmadi SA. V-Net: fully convolutional neural networks for volumetric medical image segmentation. 2016. arXiv:1606.04797v1.
 Szegedy C, Ioffe S, Vanhoucke V, Alemi A. Inception-v4, inception-resnet and the impact of residual connections on learning. 2016. arXiv:1602.07261.
 Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O. 3D u-net: learning dense volumetric segmentation from sparse annotation. 2016. arXiv: 1606.06650.
 Kanne JP. Chest CT findings in 2019 novel coronavirus (2019-nCoV) infections from Wuhan, China: key points for the radiologist. Radiology 2020;295 (1):16–7. link1
 Chung M, Bernheim A, Mei X, Zhang N, Huang M, Zeng X. CT imaging features of 2019 novel coronavirus (2019-nCoV). Radiology 2020;295(1):202–7. link1
 He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. 2015. arXiv:1512.03385.
 Breiman L. Bagging predictors. Mach Learn 1996;24(2):123–40. link1
 Wang C, Horby PW, Hayden FG, Gao GF. A novel coronavirus outbreak of global health concern. Lancet 2020;395(10223):470–3. link1
 Holshue ML, DeBolt C, Lindquist S, Lofy KH, Wiesman J, Bruce H, et al. First case of 2019 novel coronavirus in the United States. N Engl J Med 2020;382 (10):929–36. link1
 Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet 2020;395(10223):507–13. link1
 Shi H, Han X, Jiang N, Cao Y, Alwalid O, Gu J, et al. Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study. Lancet Infect Dis 2020;20(4):425–34. link1