Selected article for: "clinical testing and Ct value"
Author: 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
Title: Artificial intelligence for rapid identification of the coronavirus disease 2019 (COVID-19)
  • Document date: 2020_4_17
    • ID: 79tozwzq_2
    Snippet: Nevertheless, chest CT findings are normal in some patients early in the disease course and therefore chest CT alone has limited negative predictive value to fully exclude infection 8 , highlighting the need to incorporate clinical information in the diagnosis. In the current climate of stress on healthcare resources due to the COVID-19 outbreak, including a shortage of RT-PCR test kits, there is an unmet need for rapid, accurate, unsupervised di.....
    Document: Nevertheless, chest CT findings are normal in some patients early in the disease course and therefore chest CT alone has limited negative predictive value to fully exclude infection 8 , highlighting the need to incorporate clinical information in the diagnosis. In the current climate of stress on healthcare resources due to the COVID-19 outbreak, including a shortage of RT-PCR test kits, there is an unmet need for rapid, accurate, unsupervised diagnostic tests for SARS-CoV-2. We propose that artificial intelligence (AI) algorithms may meet this need by integrating chest CT findings with clinical symptoms, exposure history, and/or laboratory testing in the algorithm.

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