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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.
Affiliation
  • Shuo Jin; Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
  • Bo Wang; State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing, China
  • Haibo Xu; Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
  • Chuan Luo; State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing, China
  • Lai Wei; Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
  • Wei Zhao; Beijing Jingzhen Medical Technology Ltd., Beijing, China
  • Xuexue Hou; Beijing Jingzhen Medical Technology Ltd., Beijing, China
  • Wenshuo Ma; Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
  • Zhengqing Xu; Beijing Jingzhen Medical Technology Ltd., Beijing, China
  • Zhuozhao Zheng; Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
  • Wenbo Sun; Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
  • Lan Lan; Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
  • Wei Zhang; Beijing Jingzhen Medical Technology Ltd., Beijing, China
  • Xiangdong Mu; Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
  • Chenxi Shi; Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
  • Zhongxiao Wang; Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
  • Jihae Lee; Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
  • Zijian Jin; Beijing Jingzhen Medical Technology Ltd., Beijing, China
  • Minggui Lin; Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
  • Hongbo Jin; Beijing Jingzhen Medical Technology Ltd., Beijing, China
  • Liang Zhang; School of Computer Science and Technology, Xidian University, Xi'an, China
  • Jun Guo; Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
  • Benqi Zhao; Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
  • Zhizhong Ren; Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
  • Shuhao Wang; Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
  • Zheng You; State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing, China
  • Jiahong Dong; Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
  • Xinghuan Wang; Center for Evidence-Based and Translational Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China
  • Jianming Wang; Department of Biliary and Pancreatic Surgery/Cancer Research Center Affiliated Tongji Hospital, Tongji Medical College, Huazhong University of Science and Techn
  • Wei Xu; Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
Preprint in English | medRxiv | ID: ppmedrxiv-20039354
ABSTRACT
The sudden outbreak of novel coronavirus 2019 (COVID-19) increased the diagnostic burden of radiologists. In the time of an epidemic crisis, we hoped artificial intelligence (AI) to help reduce physician workload in regions with the outbreak, and improve the diagnosis accuracy for physicians before they could acquire enough experience with the new disease. Here, we present our experience in building and deploying an AI system that automatically analyzes CT images to detect COVID-19 pneumonia features. Different from conventional medical AI, we were dealing with an epidemic crisis. Working in an interdisciplinary team of over 30 people with medical and / or AI background, geographically distributed in Beijing and Wuhan, we were able to overcome a series of challenges in this particular situation and deploy the system in four weeks. Using 1,136 training cases (723 positives for COVID-19) from five hospitals, we were able to achieve a sensitivity of 0.974 and specificity of 0.922 on the test dataset, which included a variety of pulmonary diseases. Besides, the system automatically highlighted all lesion regions for faster examination. As of today, we have deployed the system in 16 hospitals, and it is performing over 1,300 screenings per day.
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Full text: Available Collection: Preprints Database: medRxiv Type of study: Diagnostic study Language: English Year: 2020 Document type: Preprint
Full text: Available Collection: Preprints Database: medRxiv Type of study: Diagnostic study Language: English Year: 2020 Document type: Preprint
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