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_14
Snippet: The copyright holder for this preprint (which was not peer-reviewed) is the .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 showed the detailed segmentation model performance. By fixing the segmentation model as 3D U-Net++, we used 1,136 (723 were positive) / 282 cases (154 were positive) to train / test the classification / combined model, the detailed data distribution was given in Tables S4, S5 and S6. Figure 2 (a) showed .....
Document: The copyright holder for this preprint (which was not peer-reviewed) is the .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 showed the detailed segmentation model performance. By fixing the segmentation model as 3D U-Net++, we used 1,136 (723 were positive) / 282 cases (154 were positive) to train / test the classification / combined model, the detailed data distribution was given in Tables S4, S5 and S6. Figure 2 (a) showed the receiver operating characteristic (ROC) curves of these four combined models. The "3D Unet++ -ResNet-50" combined model achieved the best area under the curve (AUC) of 0.991. Figure 2 The performance of the model improved steadily as the training data accumulated. In practice, the model was continually retrained in multiple stages (the average time between stages was about three days). Table S7 showed the training datasets we used in each stage. Figure 2 author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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