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_8
Snippet: During an outbreak of a highly infectious disease with person-to-person transmission, hospitals and physicians may have increased workloads and limited capabilities to triage and hospitalize suspected patients. Previous work demonstrated that early stage coronavirus patients may have negative findings on CT 8 , limiting radiologists' ability to reliably exclude disease. While waiting All rights reserved. No reuse allowed without permission. autho.....
Document: During an outbreak of a highly infectious disease with person-to-person transmission, hospitals and physicians may have increased workloads and limited capabilities to triage and hospitalize suspected patients. Previous work demonstrated that early stage coronavirus patients may have negative findings on CT 8 , limiting radiologists' ability to reliably exclude disease. While waiting All rights reserved. No reuse allowed without permission. author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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