Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China.
Department of Clinical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Nat Biomed Eng. 2020 Dec;4(12):1197-1207. doi: 10.1038/s41551-020-00633-5. Epub 2020 Nov 18.
Data from patients with coronavirus disease 2019 (COVID-19) are essential for guiding clinical decision making, for furthering the understanding of this viral disease, and for diagnostic modelling. Here, we describe an open resource containing data from 1,521 patients with pneumonia (including COVID-19 pneumonia) consisting of chest computed tomography (CT) images, 130 clinical features (from a range of biochemical and cellular analyses of blood and urine samples) and laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) clinical status. We show the utility of the database for prediction of COVID-19 morbidity and mortality outcomes using a deep learning algorithm trained with data from 1,170 patients and 19,685 manually labelled CT slices. In an independent validation cohort of 351 patients, the algorithm discriminated between negative, mild and severe cases with areas under the receiver operating characteristic curve of 0.944, 0.860 and 0.884, respectively. The open database may have further uses in the diagnosis and management of patients with COVID-19.
来自 2019 年冠状病毒病（COVID-19）患者的数据对于指导临床决策、深入了解这种病毒性疾病以及诊断建模至关重要。在这里，我们描述了一个开放资源，其中包含了 1521 名肺炎患者（包括 COVID-19 肺炎）的数据，这些数据包括胸部计算机断层扫描（CT）图像、130 个临床特征（来自血液和尿液样本的一系列生化和细胞分析）以及实验室确诊的严重急性呼吸综合征冠状病毒 2（SARS-CoV-2）临床状况。我们展示了该数据库在使用来自 1170 名患者和 19685 张手动标记 CT 切片的数据训练的深度学习算法预测 COVID-19 发病率和死亡率结局方面的实用性。在 351 名患者的独立验证队列中，该算法区分了阴性、轻症和重症病例，其接受者操作特征曲线下面积分别为 0.944、0.860 和 0.884。该开放数据库可能在 COVID-19 患者的诊断和管理中具有进一步的用途。
