Artificial intelligence for rapid identification of the coronavirus disease 2019 (COVID-19)

Xueyan Mei; Hao-Chih Lee; Kaiyue Diao; Mingqian Huang; Bin Lin; Chenyu Liu; Zongyu Xie; Yixuan Ma; Philip M. Robson; Michael Chung; Adam Bernheim; Venkatesh Mani; Claudia Calcagno; Kunwei Li; Shaolin Li; Hong Shan; Jian Lv; Tongtong Zhao; Junli Xia; Qihua Long; Sharon Steinberger; Adam Jacobi; Timothy Deyer; Marta Luksza; Fang Liu; Brent P. Little; Zahi A. Fayad; Yang Yang

The coronavirus disease 2019 outbreak that originated in Wuhan, China 1,2 has rapidly propagated due to widespread person-to-person transmission and has resulted in 1,113,758 cases in 197 countries and territories with a total of over 62,784 deaths as of April 5, 2020 [3] [4] [5] [6] [7] . Early recognition of the disease is crucial not only for individual patient care related to rapid implementation of treatment, but also from a larger public health perspective to ensure adequate patient isolation and disease containment. Laboratory confirmation of SARS-CoV-2 is performed with a virus-specific reverse transcriptase polymerase chain reaction (RT-PCR), but the test can take up to two days to complete, and due to the possibility of false negatives serial testing may be required to reliably exclude infection. Chest computed tomography (CT) is a valuable component of evaluation and diagnosis in symptomatic patients with suspected SARS-CoV-2 infection. Chest CT is more sensitive and specific than chest radiography in evaluation of SARS-CoV-2 pneumonia, and there have been cases where CT findings were present prior to clinical symptomatology onset 4 .