AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system in four weeks

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

The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.03. 19.20039354 doi: medRxiv preprint pixels in both images, which was widely used to measure the ability of the segmentation algorithm in medical image segmentation tasks. AUC denoted "area under the ROC curve", in which ROC stood for "receiver operating characteristic". ROC curve was drawn by plotting the true positive rate versus the false positive rate under different classification thresholds. Then AUC calculated the two-dimensional area under the entire ROC curve from (0,0) to (1,1), which could provide an aggregate measure of the classifier performance across varied discrimination thresholds. Sensitivity / specificity was also known as the true positive / negative rate, measured the fraction of positives / negatives that were correctly identified as positive / negative. 18 All rights reserved. No reuse allowed without permission. author/funder, who has granted medRxiv a license to display the preprint in perpetuity.