Diagnosis of Coronavirus Disease 2019 Pneumonia by Using Chest Radiography: Value of Artificial Intelligence.
Diagnosis of Coronavirus Disease 2019 Pneumonia by Using Chest Radiography: Value of Artificial Intelligence.
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使用胸片诊断冠状病毒病2019肺炎：人工智能的价值。
DOI：
10.1148/radiol.2020202944
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发表时间：
2021-03
期刊：
影响因子：
19.7
通讯作者：
Chen GH
中科院分区：
文献类型：
--
作者：
Zhang R;Tie X;Qi Z;Bevins NB;Zhang C;Griner D;Song TK;Nadig JD;Schiebler ML;Garrett JW;Li K;Reeder SB;Chen GH
放射科医生擅长区分有和没有肺炎症状的胸片，但发现在胸片上区分2019冠状病毒病（COVID-19）肺炎和非COVID-19肺炎更具挑战性。开发一种人工智能算法，以区分COVID-19肺炎与胸部X线摄影异常的其他原因。在这项回顾性研究中，对深度神经网络CV19-Net进行了训练、验证，并在有和没有COVID-19肺炎的患者的胸片上进行了测试。对于COVID-19阳性的胸片，包括2020年2月1日至2020年5月30日期间严重急性呼吸综合征冠状病毒2型逆转录聚合酶链反应结果阳性且肺炎结果阳性的患者。对于非COVID-19胸片，纳入了2019年10月1日至2019年12月31日期间接受胸片检查的肺炎患者。计算受试者工作特征曲线下面积（AUC）、灵敏度和特异性，以表征诊断性能。为了对CV19-Net的性能进行基准测试，由CV19-Net和三名经验丰富的胸部放射科医生评价了由500名患者的500张胸部X线照片组成的随机抽样测试数据集。共纳入2060例COVID-19肺炎患者（5806例胸片;平均年龄62岁± 16岁[标准差]; 1059例男性）和3148例非COVID-19肺炎患者（5300例胸片;平均年龄64岁± 18岁; 1578例男性），并将其分为训练、验证和测试数据集。对于测试集，CV19-Net的AUC为0.92（95% CI：0.91，0.93）。这对应于使用高灵敏度操作阈值的灵敏度为88%（95% CI：87，89）和特异性为79%（95% CI：77，80），或使用高特异性操作阈值的灵敏度为78%（95% CI：77，79）和特异性为89%（95% CI：88，90）。对于500张采样胸片，CV19-Net实现的AUC为0.94（95% CI：0.93，0.96），而放射科医生实现的AUC为0.85（95% CI：0.81，0.88）。CV19-Net能够区分2019冠状病毒病相关肺炎与其他类型的肺炎，性能超过经验丰富的胸放射科医生。© RSNA，2021在线补充材料可用于本文。
Radiologists are proficient in differentiating between chest radiographs with and without symptoms of pneumonia but have found it more challenging to differentiate coronavirus disease 2019 (COVID-19) pneumonia from non–COVID-19 pneumonia on chest radiographs. To develop an artificial intelligence algorithm to differentiate COVID-19 pneumonia from other causes of abnormalities at chest radiography. In this retrospective study, a deep neural network, CV19-Net, was trained, validated, and tested on chest radiographs in patients with and without COVID-19 pneumonia. For the chest radiographs positive for COVID-19, patients with reverse transcription polymerase chain reaction results positive for severe acute respiratory syndrome coronavirus 2 with findings positive for pneumonia between February 1, 2020, and May 30, 2020, were included. For the non–COVID-19 chest radiographs, patients with pneumonia who underwent chest radiography between October 1, 2019, and December 31, 2019, were included. Area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were calculated to characterize diagnostic performance. To benchmark the performance of CV19-Net, a randomly sampled test data set composed of 500 chest radiographs in 500 patients was evaluated by the CV19-Net and three experienced thoracic radiologists. A total of 2060 patients (5806 chest radiographs; mean age, 62 years ± 16 [standard deviation]; 1059 men) with COVID-19 pneumonia and 3148 patients (5300 chest radiographs; mean age, 64 years ± 18; 1578 men) with non–COVID-19 pneumonia were included and split into training and validation and test data sets. For the test set, CV19-Net achieved an AUC of 0.92 (95% CI: 0.91, 0.93). This corresponded to a sensitivity of 88% (95% CI: 87, 89) and a specificity of 79% (95% CI: 77, 80) by using a high-sensitivity operating threshold, or a sensitivity of 78% (95% CI: 77, 79) and a specificity of 89% (95% CI: 88, 90) by using a high-specificity operating threshold. For the 500 sampled chest radiographs, CV19-Net achieved an AUC of 0.94 (95% CI: 0.93, 0.96) compared with an AUC of 0.85 (95% CI: 0.81, 0.88) achieved by radiologists. CV19-Net was able to differentiate coronavirus disease 2019–related pneumonia from other types of pneumonia, with performance exceeding that of experienced thoracic radiologists. © RSNA, 2021 Online supplemental material is available for this article.
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