Selected article for: "AI model and clinical information"
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_16
    Snippet: We evaluated the AI models on the testing set and compared their performance to one fellowship trained thoracic radiologist with ten years of experience (A. J.) and one thoracic radiology fellow (S. S.). The same initial chest CT and clinical information were available to the radiologists as was provided to the AI model. Sensitivity, specificity and AUC were calculated for both human readers and the AI models. The performance of the AI model and .....
    Document: We evaluated the AI models on the testing set and compared their performance to one fellowship trained thoracic radiologist with ten years of experience (A. J.) and one thoracic radiology fellow (S. S.). The same initial chest CT and clinical information were available to the radiologists as was provided to the AI model. Sensitivity, specificity and AUC were calculated for both human readers and the AI models. The performance of the AI model and human readers are demonstrated in Fig. 2 author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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