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_44
Snippet: S.S a current chest radiology fellow). The readers were given patients' initial CT scan (at presentation) and associated clinical history that were used to test the AI models. Each reader independently reviewed the same set and evaluated the initial CT scan and clinical details, and combined imaging and clinical data in their review in a manner consistent with their clinical practice. Using this data, they predicted the SARS-CoV-2 status of the p.....
Document: S.S a current chest radiology fellow). The readers were given patients' initial CT scan (at presentation) and associated clinical history that were used to test the AI models. Each reader independently reviewed the same set and evaluated the initial CT scan and clinical details, and combined imaging and clinical data in their review in a manner consistent with their clinical practice. Using this data, they predicted the SARS-CoV-2 status of the patients. Their predictions were compared to those of the AI algorithm and the RT-PCR results.
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