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_32
Snippet: In conclusion, these results illustrate the potential role for a highly accurate AI algorithm for the rapid identification of SARS-CoV-2 patients which could be helpful in combating the current disease outbreak. We believe the AI model proposed, that combines CT imaging and clinical information, and shows equivalent accuracy to a senior chest radiologist, could be a useful All rights reserved. No reuse allowed without permission......
Document: In conclusion, these results illustrate the potential role for a highly accurate AI algorithm for the rapid identification of SARS-CoV-2 patients which could be helpful in combating the current disease outbreak. We believe the AI model proposed, that combines CT imaging and clinical information, and shows equivalent accuracy to a senior chest radiologist, could be a useful All rights reserved. No reuse allowed without permission.
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