Development and Validation of a Deep Learning-Based Model Using Computed Tomography Imaging for Predicting Disease Severity of Coronavirus Disease 2019
Development and Validation of a Deep Learning-Based Model Using Computed Tomography Imaging for Predicting Disease Severity of Coronavirus Disease 2019
复制标题
DOI：
10.3389/fbioe.2020.00898
复制
发表时间：
2020-07-31
影响因子：
5.7
通讯作者：
Zhu, Hong
中科院分区：
文献类型：
--
作者：
Xiao, Lu-Shan;Li, Pu;Zhu, Hong
目的冠状病毒病2019(新冠肺炎)正席卷全球，已导致数以百万计的人感染。一旦症状恶化，新冠肺炎患者面临高死亡风险；因此，及早识别重症患者可以进行早期干预，防止疾病进展，并有助于降低死亡率。这项研究旨在开发一种人工智能辅助工具，使用计算机断层扫描(CT)成像来预测疾病严重程度，并进一步估计新冠肺炎患者发展为严重疾病的风险。材料与方法回顾分析洪湖和南昌两地医院2020年1月1日至2020年3月18日确诊的408例新冠肺炎患者的CT影像资料。以洪湖市人民医院303例患者的数据为训练数据，以南昌大学第一附属医院105例患者的数据为测试数据集。提出并验证了一种基于多实例学习和残差卷积神经网络的深度学习模型(ResNet34)。分别利用接收机工作特征曲线和混淆矩阵对模型的识别能力和预测精度进行了评估。结果基于深度学习的模型在训练集中的曲线下面积为0.987(95%可信区间：0.968~1.00)，准确率为97.4%；在测试集中的曲线下面积为0.892(0.828~0.955)，准确率为81.9%。在对入院时非重症新冠肺炎患者的亚组分析中，洪湖和南昌亚组的AUC值分别为0.955(0.884-1.00)和0.923(0.864-0.983)，准确率分别为97.0%和81.6%。结论基于深度学习的模型可以利用CT影像准确预测新冠肺炎患者的病情严重程度和病情进展，为指导临床治疗提供依据。
Objectives Coronavirus disease 2019 (COVID-19) is sweeping the globe and has resulted in infections in millions of people. Patients with COVID-19 face a high fatality risk once symptoms worsen; therefore, early identification of severely ill patients can enable early intervention, prevent disease progression, and help reduce mortality. This study aims to develop an artificial intelligence-assisted tool using computed tomography (CT) imaging to predict disease severity and further estimate the risk of developing severe disease in patients suffering from COVID-19. Materials and Methods Initial CT images of 408 confirmed COVID-19 patients were retrospectively collected between January 1, 2020 and March 18, 2020 from hospitals in Honghu and Nanchang. The data of 303 patients in the People's Hospital of Honghu were assigned as the training data, and those of 105 patients in The First Affiliated Hospital of Nanchang University were assigned as the test dataset. A deep learning based-model using multiple instance learning and residual convolutional neural network (ResNet34) was developed and validated. The discrimination ability and prediction accuracy of the model were evaluated using the receiver operating characteristic curve and confusion matrix, respectively. Results The deep learning-based model had an area under the curve (AUC) of 0.987 (95% confidence interval [CI]: 0.968-1.00) and an accuracy of 97.4% in the training set, whereas it had an AUC of 0.892 (0.828-0.955) and an accuracy of 81.9% in the test set. In the subgroup analysis of patients who had non-severe COVID-19 on admission, the model achieved AUCs of 0.955 (0.884-1.00) and 0.923 (0.864-0.983) and accuracies of 97.0 and 81.6% in the Honghu and Nanchang subgroups, respectively. Conclusion Our deep learning-based model can accurately predict disease severity as well as disease progression in COVID-19 patients using CT imaging, offering promise for guiding clinical treatment.