About: Development and Validation of a Deep Learning-Based Model Using Computed Tomography Imaging for Predicting Disease Severity of Coronavirus Disease 2019

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About: Development and Validation of a Deep Learning-Based Model Using Computed Tomography Imaging for Predicting Disease Severity of Coronavirus Disease 2019 Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Development and Validation of a Deep Learning-Based Model Using Computed Tomography Imaging for Predicting Disease Severity of Coronavirus Disease 2019
Creator	Zhu, Hong Li, Xiao Liu, Li Santosh, K Gong, Mengchun Hu, Chenyi Xiao, Lu-Shan Zhang, Wen-Feng Cai, Feng-Qin Cai, Zhu Gong, Hu He, Yu-Lin Li, Pu Ma, Si-Cong Nath, Neetika Shi, Wenzhao Struzik, Zbigniew Sun, Fenglong Xu, Chenghai Zhang, Y-L Zhang, Yanpei Zhu, Hongbo
source	Medline; PMC
abstract	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.
has issue date	2020-07-31(xsd:dateTime)
bibo:doi	10.3389/fbioe.2020.00898
bibo:pmid	32850746
has license	cc-by
sha1sum (hex)	3d2e82033dfafd53a6a6376174608cf26650e8cf
schema:url	https://doi.org/10.3389/fbioe.2020.00898
resource representing a document's title	Development and Validation of a Deep Learning-Based Model Using Computed Tomography Imaging for Predicting Disease Severity of Coronavirus Disease 2019
has PubMed Central identifier	PMC7411489
has PubMed identifier	32850746
schema:publication	Front Bioeng Biotechnol
resource representing a document's body	covid:3d2e82033dfafd53a6a6376174608cf26650e8cf#body_text
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