PDF(Sci-hub)
Abstract：
Background Coronavirus disease 2019 (COVID-19) and pneumonia of other diseases share similar CT characteristics, which contributes to the challenges in differentiating them with high accuracy. Purpose To establish and evaluate an artificial intelligence (AI) system for differentiating COVID-19 and other pneumonia at chest CT and assessing radiologist performance without and with AI assistance. Materials and Methods A total of 521 patients with positive reverse transcription polymerase chain reaction results for COVID-19 and abnormal chest CT findings were retrospectively identified from 10 hospitals from January 2020 to April 2020. A total of 665 patients with non-COVID-19 pneumonia and definite evidence of pneumonia at chest CT were retrospectively selected from three hospitals between 2017 and 2019. To classify COVID-19 versus other pneumonia for each patient, abnormal CT slices were input into the EfficientNet B4 deep neural network architecture after lung segmentation, followed by a two-layer fully connected neural network to pool slices together. The final cohort of 1186 patients (132 583 CT slices) was divided into training, validation, and test sets in a 7:2:1 and equal ratio. Independent testing was performed by evaluating model performance in separate hospitals. Studies were blindly reviewed by six radiologists without and then with AI assistance. Results The final model achieved a test accuracy of 96% (95% confidence interval [CI]: 90%, 98%), a sensitivity of 95% (95% CI: 83%, 100%), and a specificity of 96% (95% CI: 88%, 99%) with area under the receiver operating characteristic curve of 0.95 and area under the precision-recall curve of 0.90. On independent testing, this model achieved an accuracy of 87% (95% CI: 82%, 90%), a sensitivity of 89% (95% CI: 81%, 94%), and a specificity of 86% (95% CI: 80%, 90%) with area under the receiver operating characteristic curve of 0.90 and area under the precision-recall curve of 0.87. Assisted by the probabilities of the model, the radiologists achieved a higher average test accuracy (90% vs 85%, Δ = 5, P < .001), sensitivity (88% vs 79%, Δ = 9, P < .001), and specificity (91% vs 88%, Δ = 3, P = .001). Conclusion Artificial intelligence assistance improved radiologists\' performance in distinguishing coronavirus disease 2019 pneumonia from non-coronavirus disease 2019 pneumonia at chest CT. © RSNA, 2020 Online supplemental material is available for this article.
摘要：
背景2019年冠状病毒病（COVID-19）和其他疾病的肺炎具有相似的CT特征，这有助于以高精度区分它们的挑战。目的建立和评估一种人工智能（AI）系统，用于在胸部CT上区分COVID-19和其他肺炎，并在没有AI辅助的情况下评估放射科医师的表现。资料与方法回顾性分析2020年1月至2020年4月10所医院确诊的COVID-19逆转录聚合酶链反应阳性且胸部CT异常的521例患者。回顾性选取2017年至2019年三家医院共665例非COVID-19肺炎且胸部CT明确肺炎的患者。对每位患者的COVID-19与其他肺炎进行分类，在肺分割后，将异常的CT切片输入到EfficientNetB4深度神经网络架构中，然后是一个两层完全连接的神经网络，将切片池在一起。最终1186名患者（132583个CT切片）被分为训练组，验证,和测试集以7:2:1和相等的比例。通过评估不同医院的模型性能进行独立测试。六名放射科医生在没有人工智能帮助的情况下盲目审查了研究。结果最终模型的检验准确率为96%(95%置信区间[CI]：90%，98%），敏感度为95%(95%CI：83%，100%),特异性为96%(95%CI：88%，99%），接受者工作特征曲线下面积为0.95，精确召回率曲线下面积为0.90。在独立测试中，该模型的准确率为87%(95%CI：82%，90%)，灵敏度为89%(95%CI：81%，94%），特异性为86%(95%CI：80%，90％），接收器工作特征曲线下面积为0.90，精确召回曲线下面积为0.87。在模型概率的辅助下，放射科医生实现了更高的平均测试精度(90%对85%，Δ=5，P<.001)，灵敏度（88%vs79%，Δ=9，P<.001)，和特异性(91%对88%，Δ=3，P=.001)。结论人工智能辅助提高了放射科医师在胸部CT上区分冠状病毒病2019肺炎和非冠状病毒病2019肺炎的表现。©RSNA,2020在线补充材料可用于本文。
