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

Sci Rep. 2021 Feb 18;11(1):4145. doi: 10.1038/s41598-021-83424-5.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • COVID-19 / diagnosis*
  • COVID-19 / diagnostic imaging*
  • COVID-19 / epidemiology
  • COVID-19 / metabolism
  • China / epidemiology
  • Data Accuracy
  • Deep Learning
  • Humans
  • Lung / pathology
  • Pneumonia / diagnostic imaging
  • Retrospective Studies
  • SARS-CoV-2 / isolation & purification
  • Sensitivity and Specificity
  • Tomography, X-Ray Computed / methods*