2019年冠状病毒病(COVID-19)
人工智能
深度学习
接收机工作特性
严重急性呼吸综合征冠状病毒2型(SARS-CoV-2)
医学
人工神经网络
计算机科学
机器学习
预测值
核医学
放射科
模式识别(心理学)
病理
传染病(医学专业)
内科学
疾病
作者
Chuansheng Zheng,Xianbo Deng,Yu Jin,Qiang Zhou,Jiapei Feng,Revision Created,Revision Converted,Newly Submitted Revision,Final Decision
出处
期刊:Cold Spring Harbor Laboratory - medRxiv
日期:2020-03-17
被引量:330
标识
DOI:10.1101/2020.03.12.20027185
摘要
Abstract Accurate and rapid diagnosis of COVID-19 suspected cases plays a crucial role in timely quarantine and medical treatment. Developing a deep learning-based model for automatic COVID-19 detection on chest CT is helpful to counter the outbreak of SARS-CoV-2. A weakly-supervised deep learning-based software system was developed using 3D CT volumes to detect COVID-19. For each patient, the lung region was segmented using a pre-trained UNet; then the segmented 3D lung region was fed into a 3D deep neural network to predict the probability of COVID-19 infectious. 499 CT volumes collected from Dec. 13, 2019, to Jan. 23, 2020, were used for training and 131 CT volumes collected from Jan 24, 2020, to Feb 6, 2020, were used for testing. The deep learning algorithm obtained 0.959 ROC AUC and 0.976 PR AUC. There was an operating point with 0.907 sensitivity and 0.911 specificity in the ROC curve. When using a probability threshold of 0.5 to classify COVID-positive and COVID-negative, the algorithm obtained an accuracy of 0.901, a positive predictive value of 0.840 and a very high negative predictive value of 0.982. The algorithm took only 1.93 seconds to process a single patient’s CT volume using a dedicated GPU. Our weakly-supervised deep learning model can accurately predict the COVID-19 infectious probability in chest CT volumes without the need for annotating the lesions for training. The easily-trained and highperformance deep learning algorithm provides a fast way to identify COVID-19 patients, which is beneficial to control the outbreak of SARS-CoV-2. The developed deep learning software is available at https://github.com/sydney0zq/covid-19-detection .
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