Artificial Intelligence Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Origin at Chest CT

作者：Harrison X. Bai, Robin Wang, Zeng Xiong, B. R. Hsieh, Ken Chang, Kasey Halsey, Thi My Linh Tran, Ji Whae Choi, Dongcui Wang, Linbo Shi, Mei Ji, Xiaolong Jiang, Ian Pan, Qiuhua Zeng, Ping-Feng Hu, Yihui Li, Feixian Fu, Raymond Y. Huang, Ronnie Sebro, Qizhi Yu, Michael K. Atalay, Weihua Liao · 发表于：Radiology · 年份：2020 · DOI：10.1148/radiol.2020201491 · 被引用次数：404 · 研究领域：COVID-19 diagnosis using AI、Radiomics and Machine Learning in Medical Imaging、Artificial Intelligence in Healthcare and Education

Background Coronavirus disease 2019 (COVID-19) and pneumonia of other diseases share similar CT characteristics, which contributes to the challenges in differentiating them with high accuracy. Purpose To establish and evaluate an artificial intelligence (AI) system for differentiating COVID-19 and other pneumonia at chest CT and assessing radiologist performance without and with AI assistance. Materials and Methods A total of 521 patients with positive reverse transcription polymerase chain reaction results for COVID-19 and abnormal chest CT findings were retrospectively identified from 10 hospitals from January 2020 to April 2020. A total of 665 patients with non–COVID-19 pneumonia and definite evidence of pneumonia at chest CT were retrospectively selected from three hospitals between 2017 and 2019. To classify COVID-19 versus other pneumonia for each patient, abnormal CT slices were input into the EfficientNet B4 deep neural network architecture after lung segmentation, followed by a two-layer fully connected neural network to pool slices together. The final cohort of 1186 patients (132 583 CT slices) was divided into training, validation, and test sets in a 7:2:1 and equal ratio. Independent testing was performed by evaluating model performance in separate hospitals. Studies were blindly reviewed by six radiologists without and then with AI assistance. Results The final model achieved a test accuracy of 96% (95% confidence interval [CI]: 90%, 98%), a sensitivity of 95% (9...