Author: Shuo Jin; Bo Wang; Haibo Xu; Chuan Luo; Lai Wei; Wei Zhao; Xuexue Hou; Wenshuo Ma; Zhengqing Xu; Zhuozhao Zheng; Wenbo Sun; Lan Lan; Wei Zhang; Xiangdong Mu; Chenxi Shi; Zhongxiao Wang; Jihae Lee; Zijian Jin; Minggui Lin; Hongbo Jin; Liang Zhang; Jun Guo; Benqi Zhao; Zhizhong Ren; Shuhao Wang; Zheng You; Jiahong Dong; Xinghuan Wang; Jianming Wang; Wei Xu
Title: AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system in four weeks Document date: 2020_3_23
ID: e6q92shw_17
Snippet: It was necessary to study the false positive and false negative predictions, given in Figure 2 (c). Most notably, the model sometimes missed positive cases for patchy ground glass opacities with diameters less than 1 cm. The model might also introduce false positives with other types of viral pneumonia, for instance, lobar pneumonia, with similar CT features. Also, the model did not perform well when there were multiple types of lesions, or wit.....
Document: It was necessary to study the false positive and false negative predictions, given in Figure 2 (c). Most notably, the model sometimes missed positive cases for patchy ground glass opacities with diameters less than 1 cm. The model might also introduce false positives with other types of viral pneumonia, for instance, lobar pneumonia, with similar CT features. Also, the model did not perform well when there were multiple types of lesions, or with significant metal or motion artifacts. We plan to obtain more cases with these features for training as our next steps. All rights reserved. No reuse allowed without permission.
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