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.

中文翻译：

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使用计算机断层扫描成像预测 2019 年冠状病毒疾病严重程度的深度学习模型的开发和验证


目标 2019 年冠状病毒病 (COVID-19) 正在席卷全球，已导致数百万人感染。一旦症状恶化，COVID-19 患者将面临很高的死亡风险；因此，早期识别重症患者可以早期干预，防止疾病进展，并有助于降低死亡率。本研究旨在开发一种人工智能辅助工具，利用计算机断层扫描 (CT) 成像来预测疾病严重程度，并进一步估计 COVID-19 患者患严重疾病的风险。材料和方法回顾性收集2020年1月1日至2020年3月18日洪湖市和南昌市医院408例确诊的COVID-19患者的初始CT图像。将洪湖市人民医院的303名患者的数据作为训练数据，将南昌大学第一附属医院的105名患者的数据作为测试数据集。开发并验证了使用多实例学习和残差卷积神经网络 (ResNet34) 的基于深度学习的模型。分别使用受试者工作特征曲线和混淆矩阵评价模型的判别能力和预测准确性。结果 基于深度学习的模型在训练集中的曲线下面积 (AUC) 为 0.987（95% 置信区间 [CI]：0.968–1.00），准确率为 97.4%，而它的 AUC 为 0.892（ 0.828–0.955），测试集中的准确率为 81.9%。在入院时非重症 COVID-19 患者的亚组分析中，该模型在洪湖亚组和南昌亚组中的 AUC 分别为 0.955 (0.884–1.00) 和 0.923 (0.864–0.983)，准确度分别为 97.0 和 81.6% 。 结论 我们基于深度学习的模型可以利用 CT 成像准确预测 COVID-19 患者的疾病严重程度以及疾病进展，为指导临床治疗提供了希望。