A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19)

• DOI: 10.1101/2020.02.14.20023028
• Publication Date: 2020-02-18T02:35:20Z
• Authors: Shuai Wang; Bo Kang; Jinlu Ma; Xianjun Zeng; Mingming Xiao; Jia Guo; Mengjiao Cai; Jingyi Yang; Yaodong Li; Xiangfei Meng; Bo Xu
• Keywords: Gold standard (test); Nucleic acid test

Abstract Background The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) has caused more than 2.5 million cases Corona Virus Disease (COVID-19) in the world so far, with that number continuing to grow. To control spread disease, screening large numbers suspected for appropriate quarantine and treatment is a priority. Pathogenic laboratory testing gold standard but time-consuming significant false negative results. Therefore, alternative diagnostic methods are urgently needed combat disease. Based on COVID-19 radiographical changes CT images, we hypothesized Artificial Intelligence’s deep learning might be able extract COVID-19’s specific graphical features provide clinical diagnosis ahead pathogenic test, thus saving critical time disease control. Methods Findings We collected 1,065 images pathogen-confirmed (325 images) along those previously diagnosed typical viral pneumonia (740 images). modified Inception transfer-learning model establish algorithm, followed by internal external validation. validation achieved total accuracy 89.5% specificity 0.88 sensitivity 0.87. dataset showed 79.3% 0.83 0.67. In addition, 54 first two nucleic acid test results were negative, 46 predicted as positive 85.2%. Conclusion These demonstrate proof-of-principle using artificial intelligence radiological timely accurate diagnosis. Author summary COVID-19, measures time. pneumonia. algorithm. Our study represents apply effectively COVID-19.