TY - JOUR T1 - A Fully Automatic Deep Learning System for COVID-19 Diagnostic and Prognostic Analysis JF - European Respiratory Journal JO - Eur Respir J DO - 10.1183/13993003.00775-2020 SP - 2000775 AU - Shuo Wang AU - Yunfei Zha AU - Weimin Li AU - Qingxia Wu AU - Xiaohu Li AU - Meng Niu AU - Meiyun Wang AU - Xiaoming Qiu AU - Hongjun Li AU - He Yu AU - Wei Gong AU - Yan Bai AU - Li Li AU - Yongbei Zhu AU - Liusu Wang AU - Jie Tian Y1 - 2020/01/01 UR - http://erj.ersjournals.com/content/early/2020/05/19/13993003.00775-2020.abstract N2 - Coronavirus disease 2019 (COVID-19) has spread globally, and medical resources become insufficient in many regions. Fast diagnosis of COVID-19, and finding high-risk patients with worse prognosis for early prevention and medical resources optimisation is important. Here, we proposed a fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis by routinely used computed tomography.We retrospectively collected 5372 patients with computed tomography images from 7 cities or provinces. Firstly, 4106 patients with computed tomography images were used to pre-train the DL system, making it learn lung features. Afterwards, 1266 patients (924 with COVID-19, and 471 had follow-up for 5+ days; 342 with other pneumonia) from 6 cities or provinces were enrolled to train and externally validate the performance of the deep learning system.In the 4 external validation sets, the deep learning system achieved good performance in identifying COVID-19 from other pneumonia (AUC=0.87 and 0.88) and viral pneumonia (AUC=0.86). Moreover, the deep learning system succeeded to stratify patients into high-risk and low-risk groups whose hospital-stay time have significant difference (p=0.013 and 0.014). Without human-assistance, the deep learning system automatically focused on abnormal areas that showed consistent characteristics with reported radiological findings.Deep learning provides a convenient tool for fast screening COVID-19 and finding potential high-risk patients, which may be helpful for medical resource optimisation and early prevention before patients show severe symptoms.Fully automatic deep learning system provides a convenient method for COVID-19 diagnostic and prognostic analysis, which can help COVID-19 screening and finding potential high-risk patients with worse prognosis. ER -