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. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement No funding ### Author Declarations All relevant ethical guidelines have been followed; any necessary IRB and/or ethics committee approvals have been obtained and details of the IRB/oversight body are included in the manuscript. Yes All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data referred to in the manuscript is available.
Wang Shuai, Kang Bo, Ma Jinlu, Zeng Xianjun, Xiao Mingming, Guo Jia, Cai Mengjiao, Yang Jingyi, Li Yaodong, Meng Xiangfei, Xu Bo. (2020). A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19). Cold Spring Harbor Laboratory Press