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Sci Rep . Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography

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  • Sci Rep . Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography


    Sci Rep


    . 2020 Nov 5;10(1):19196.
    doi: 10.1038/s41598-020-76282-0.
    Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography


    Jun Chen 1 , Lianlian Wu 2 3 4 , Jun Zhang 2 3 4 , Liang Zhang 1 , Dexin Gong 2 3 4 , Yilin Zhao 1 , Qiuxiang Chen 5 , Shulan Huang 5 , Ming Yang 5 , Xiao Yang 5 , Shan Hu 6 , Yonggui Wang 7 , Xiao Hu 6 , Biqing Zheng 6 , Kuo Zhang 6 , Huiling Wu 2 3 4 , Zehua Dong 2 3 4 , Youming Xu 2 3 4 , Yijie Zhu 2 3 4 , Xi Chen 2 3 4 , Mengjiao Zhang 2 , Lilei Yu 8 , Fan Cheng 9 , Honggang Yu 10 11 12



    Affiliations

    Abstract

    Computed tomography (CT) is the preferred imaging method for diagnosing 2019 novel coronavirus (COVID19) pneumonia. We aimed to construct a system based on deep learning for detecting COVID-19 pneumonia on high resolution CT. For model development and validation, 46,096 anonymous images from 106 admitted patients, including 51 patients of laboratory confirmed COVID-19 pneumonia and 55 control patients of other diseases in Renmin Hospital of Wuhan University were retrospectively collected. Twenty-seven prospective consecutive patients in Renmin Hospital of Wuhan University were collected to evaluate the efficiency of radiologists against 2019-CoV pneumonia with that of the model. An external test was conducted in Qianjiang Central Hospital to estimate the system's robustness. The model achieved a per-patient accuracy of 95.24% and a per-image accuracy of 98.85% in internal retrospective dataset. For 27 internal prospective patients, the system achieved a comparable performance to that of expert radiologist. In external dataset, it achieved an accuracy of 96%. With the assistance of the model, the reading time of radiologists was greatly decreased by 65%. The deep learning model showed a comparable performance with expert radiologist, and greatly improved the efficiency of radiologists in clinical practice.


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