A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19)
Shuai Wang, Bo Kang, Jinlu Ma, Xianjun Zeng, Mingming Xiao, Jia Guo, Mengjiao Cai, Jingyi Yang, Yaodong Li, Xiangfei Meng, Bo Xu
doi: https://doi.org/10.1101/2020.02.14.20023028
Abstract
Background: To control the spread of Corona Virus Disease (COVID-19), screening large numbers of suspected cases for appropriate quarantine and treatment measures is a priority. Pathogenic laboratory testing is the diagnostic gold standard but it is time consuming with significant false positive results. Fast and accurate diagnostic methods are urgently needed to combat the disease. Based on COVID-19 radiographical changes in CT images, we hypothesized that deep learning methods might be able to extract COVID-19's graphical features and provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time for disease control. Methods：We collected 453 CT images of pathogen-confirmed COVID-19 cases along with previously diagnosed with typical viral pneumonia. We modified the Inception migration-learning model to establish the algorithm, followed by internal and external validation. Findings: The internal validation achieved a total accuracy of 82.9% with specificity of 80.5% and sensitivity of 84%. The external testing dataset showed a total accuracy of 73.1% with specificity of 67% and sensitivity of 74%. Interpretation: These results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate COVID-19 diagnosis. Funding: No funding is involved in the execution of the project.
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