Development and evaluation of an artificial intelligence system for COVID-19 diagnosis.
Development and evaluation of an artificial intelligence system for COVID-19 diagnosis.
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DOI：
10.1038/s41467-020-18685-1
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发表时间：
2020-10-09
影响因子：
16.6
通讯作者：
Feng J
中科院分区：
文献类型：
--
作者：
Jin C;Chen W;Cao Y;Xu Z;Tan Z;Zhang X;Deng L;Zheng C;Zhou J;Shi H;Feng J
基于胸部CT的COVID-19早期检测可以及时治疗患者，并有助于控制疾病的传播。我们提出了一种用于快速检测COVID-19的人工智能（AI）系统，并基于AI系统对COVID-19的CT进行了广泛的统计分析。我们在一个大型数据集上开发和评估了我们的系统，该数据集包含来自COVID-19、流感A/B、非病毒性社区获得性肺炎（CAP）和非肺炎受试者的1万多个CT体积。在这样一个困难的多类诊断任务中，我们基于深度卷积神经网络的系统能够在3，199次扫描的测试队列中实现97.81%的多路分类的接收器操作特征曲线（AUC）下的面积，在两个公开可用的数据集CC-CCII和MosMedData上的AUC分别为92.99%和93.25%。在一项涉及五名放射科医生的读者研究中，人工智能系统在更具挑战性的任务中以两个数量级的速度超过了所有放射科医生。比较了胸部X线（CXR）和CT的诊断性能。还执行深度网络的详细解释以将系统输出与CT呈现相关联。该代码可在https://github.com/ChenWWWeixiang/diagnosis_covid19上获得。在某些情况下，从CT扫描中快速检测COVID-19对于优化患者管理至关重要。在这里，作者提出了一个用于此任务的深度学习系统，具有多中心数据，人类读者比较和年龄分层结果。
Early detection of COVID-19 based on chest CT enables timely treatment of patients and helps control the spread of the disease. We proposed an artificial intelligence (AI) system for rapid COVID-19 detection and performed extensive statistical analysis of CTs of COVID-19 based on the AI system. We developed and evaluated our system on a large dataset with more than 10 thousand CT volumes from COVID-19, influenza-A/B, non-viral community acquired pneumonia (CAP) and non-pneumonia subjects. In such a difficult multi-class diagnosis task, our deep convolutional neural network-based system is able to achieve an area under the receiver operating characteristic curve (AUC) of 97.81% for multi-way classification on test cohort of 3,199 scans, AUC of 92.99% and 93.25% on two publicly available datasets, CC-CCII and MosMedData respectively. In a reader study involving five radiologists, the AI system outperforms all of radiologists in more challenging tasks at a speed of two orders of magnitude above them. Diagnosis performance of chest x-ray (CXR) is compared to that of CT. Detailed interpretation of deep network is also performed to relate system outputs with CT presentations. The code is available at https://github.com/ChenWWWeixiang/diagnosis_covid19. In some contexts, rapid detection of COVID-19 from CT scans can be crucial for optimal patient management. Here, the authors present a Deep Learning system for this task with multi-center data, human reader comparison and age stratified results.
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