Table_1_Development and Validation of a Deep Learning-Based Model Using Computed Tomography Imaging for Predicting Disease Severity of Coronavirus Dis.DOC (31.5 kB)

Table_1_Development and Validation of a Deep Learning-Based Model Using Computed Tomography Imaging for Predicting Disease Severity of Coronavirus Disease 2019.DOC

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posted on 31.07.2020, 08:49 by Lu-shan Xiao, Pu Li, Fenglong Sun, Yanpei Zhang, Chenghai Xu, Hongbo Zhu, Feng-Qin Cai, Yu-Lin He, Wen-Feng Zhang, Si-Cong Ma, Chenyi Hu, Mengchun Gong, Li Liu, Wenzhao Shi, Hong Zhu
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.

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