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      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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          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.

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          Most cited references25

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          A Novel Coronavirus from Patients with Pneumonia in China, 2019

          Summary In December 2019, a cluster of patients with pneumonia of unknown cause was linked to a seafood wholesale market in Wuhan, China. A previously unknown betacoronavirus was discovered through the use of unbiased sequencing in samples from patients with pneumonia. Human airway epithelial cells were used to isolate a novel coronavirus, named 2019-nCoV, which formed a clade within the subgenus sarbecovirus, Orthocoronavirinae subfamily. Different from both MERS-CoV and SARS-CoV, 2019-nCoV is the seventh member of the family of coronaviruses that infect humans. Enhanced surveillance and further investigation are ongoing. (Funded by the National Key Research and Development Program of China and the National Major Project for Control and Prevention of Infectious Disease in China.)
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            Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study

            Summary Background In December, 2019, a pneumonia associated with the 2019 novel coronavirus (2019-nCoV) emerged in Wuhan, China. We aimed to further clarify the epidemiological and clinical characteristics of 2019-nCoV pneumonia. Methods In this retrospective, single-centre study, we included all confirmed cases of 2019-nCoV in Wuhan Jinyintan Hospital from Jan 1 to Jan 20, 2020. Cases were confirmed by real-time RT-PCR and were analysed for epidemiological, demographic, clinical, and radiological features and laboratory data. Outcomes were followed up until Jan 25, 2020. Findings Of the 99 patients with 2019-nCoV pneumonia, 49 (49%) had a history of exposure to the Huanan seafood market. The average age of the patients was 55·5 years (SD 13·1), including 67 men and 32 women. 2019-nCoV was detected in all patients by real-time RT-PCR. 50 (51%) patients had chronic diseases. Patients had clinical manifestations of fever (82 [83%] patients), cough (81 [82%] patients), shortness of breath (31 [31%] patients), muscle ache (11 [11%] patients), confusion (nine [9%] patients), headache (eight [8%] patients), sore throat (five [5%] patients), rhinorrhoea (four [4%] patients), chest pain (two [2%] patients), diarrhoea (two [2%] patients), and nausea and vomiting (one [1%] patient). According to imaging examination, 74 (75%) patients showed bilateral pneumonia, 14 (14%) patients showed multiple mottling and ground-glass opacity, and one (1%) patient had pneumothorax. 17 (17%) patients developed acute respiratory distress syndrome and, among them, 11 (11%) patients worsened in a short period of time and died of multiple organ failure. Interpretation The 2019-nCoV infection was of clustering onset, is more likely to affect older males with comorbidities, and can result in severe and even fatal respiratory diseases such as acute respiratory distress syndrome. In general, characteristics of patients who died were in line with the MuLBSTA score, an early warning model for predicting mortality in viral pneumonia. Further investigation is needed to explore the applicability of the MuLBSTA score in predicting the risk of mortality in 2019-nCoV infection. Funding National Key R&D Program of China.
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              World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19)

              An unprecedented outbreak of pneumonia of unknown aetiology in Wuhan City, Hubei province in China emerged in December 2019. A novel coronavirus was identified as the causative agent and was subsequently termed COVID-19 by the World Health Organization (WHO). Considered a relative of severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), COVID-19 is caused by a betacoronavirus named SARS-CoV-2 that affects the lower respiratory tract and manifests as pneumonia in humans. Despite rigorous global containment and quarantine efforts, the incidence of COVID-19 continues to rise, with 90,870 laboratory-confirmed cases and over 3,000 deaths worldwide. In response to this global outbreak, we summarise the current state of knowledge surrounding COVID-19.
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                Author and article information

                Contributors
                Journal
                Front Bioeng Biotechnol
                Front Bioeng Biotechnol
                Front. Bioeng. Biotechnol.
                Frontiers in Bioengineering and Biotechnology
                Frontiers Media S.A.
                2296-4185
                31 July 2020
                2020
                31 July 2020
                : 8
                : 898
                Affiliations
                [1] 1Department of Medical Quality Management, Nanfang Hospital, Southern Medical University , Guangzhou, China
                [2] 2Department of Infectious Diseases, Nanfang Hospital, Southern Medical University , Guangzhou, China
                [3] 3State Drug Clinical Trial Agency, The First Affiliated Hospital, Nanchang University , Nanchang, China
                [4] 4Digital China Health Technologies Corporation Limited , Beijing, China
                [5] 5Department of Oncology, The First Affiliated Hospital, University of South China , Hengyang, China
                [6] 6Department of Radiology, The First Affiliated Hospital, Nanchang University , Nanchang, China
                [7] 7Department of Infectious Diseases, The First Affiliated Hospital, Nanchang University , Nanchang, China
                [8] 8Nanfang Hospital, Southern Medical University , Guangzhou, China
                Author notes

                Edited by: Zbigniew R. Struzik, The University of Tokyo, Japan

                Reviewed by: Neetika Nath, Universitätsmedizin Greifswald, Germany; K. C. Santosh, University of South Dakota, United States

                *Correspondence: Li Liu, liuli@ 123456i.smu.edu.cn

                These authors have contributed equally to this work

                This article was submitted to Computational Genomics, a section of the journal Frontiers in Bioengineering and Biotechnology

                Article
                10.3389/fbioe.2020.00898
                7411489
                32850746
                9dd48e69-1812-426d-b67b-f9d50b4872fe
                Copyright © 2020 Xiao, Li, Sun, Zhang, Xu, Zhu, Cai, He, Zhang, Ma, Hu, Gong, Liu, Shi and Zhu.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 29 April 2020
                : 13 July 2020
                Page count
                Figures: 4, Tables: 1, Equations: 7, References: 34, Pages: 11, Words: 0
                Funding
                Funded by: National Natural Science Foundation of China 10.13039/501100001809
                Award ID: 81972897
                Categories
                Bioengineering and Biotechnology
                Original Research

                covid-19,computed tomography,deep learning,disease severity,multiple instance learning

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