FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3046717168> ?p ?o ?g. }

• W3046717168 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." @default.
• W3046717168 created "2020-08-07" @default.
• W3046717168 creator A5004980088 @default.
• W3046717168 creator A5011759044 @default.
• W3046717168 creator A5020357508 @default.
• W3046717168 creator A5029327703 @default.
• W3046717168 creator A5029401819 @default.
• W3046717168 creator A5030957310 @default.
• W3046717168 creator A5033673138 @default.
• W3046717168 creator A5050306506 @default.
• W3046717168 creator A5063436972 @default.
• W3046717168 creator A5063481044 @default.
• W3046717168 creator A5074141555 @default.
• W3046717168 creator A5075467680 @default.
• W3046717168 creator A5087400213 @default.
• W3046717168 creator A5087648196 @default.
• W3046717168 creator A5087920560 @default.
• W3046717168 date "2020-07-31" @default.
• W3046717168 modified "2023-10-12" @default.
• W3046717168 title "Development and Validation of a Deep Learning-Based Model Using Computed Tomography Imaging for Predicting Disease Severity of Coronavirus Disease 2019" @default.
• W3046717168 cites W1978349722 @default.
• W3046717168 cites W2006617902 @default.
• W3046717168 cites W2076063813 @default.
• W3046717168 cites W2111742711 @default.
• W3046717168 cites W2155982020 @default.
• W3046717168 cites W2323929895 @default.
• W3046717168 cites W2418101495 @default.
• W3046717168 cites W2956228567 @default.
• W3046717168 cites W3001897055 @default.
• W3046717168 cites W3002108456 @default.
• W3046717168 cites W3005929298 @default.
• W3046717168 cites W3006643024 @default.
• W3046717168 cites W3009156795 @default.
• W3046717168 cites W3010223921 @default.
• W3046717168 cites W3010313912 @default.
• W3046717168 cites W3010552451 @default.
• W3046717168 cites W3010714490 @default.
• W3046717168 cites W3011048075 @default.
• W3046717168 cites W3011149445 @default.
• W3046717168 cites W3011414569 @default.
• W3046717168 cites W3011716991 @default.
• W3046717168 cites W3013004778 @default.
• W3046717168 cites W3017022286 @default.
• W3046717168 cites W3024853795 @default.
• W3046717168 cites W3026522186 @default.
• W3046717168 cites W3026894541 @default.
• W3046717168 cites W4206245015 @default.
• W3046717168 cites W4246697656 @default.
• W3046717168 doi "https://doi.org/10.3389/fbioe.2020.00898" @default.
• W3046717168 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7411489" @default.
• W3046717168 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32850746" @default.
• W3046717168 hasPublicationYear "2020" @default.
• W3046717168 type Work @default.
• W3046717168 sameAs 3046717168 @default.
• W3046717168 citedByCount "58" @default.
• W3046717168 countsByYear W30467171682020 @default.
• W3046717168 countsByYear W30467171682021 @default.
• W3046717168 countsByYear W30467171682022 @default.
• W3046717168 countsByYear W30467171682023 @default.
• W3046717168 crossrefType "journal-article" @default.
• W3046717168 hasAuthorship W3046717168A5004980088 @default.
• W3046717168 hasAuthorship W3046717168A5011759044 @default.
• W3046717168 hasAuthorship W3046717168A5020357508 @default.
• W3046717168 hasAuthorship W3046717168A5029327703 @default.
• W3046717168 hasAuthorship W3046717168A5029401819 @default.
• W3046717168 hasAuthorship W3046717168A5030957310 @default.
• W3046717168 hasAuthorship W3046717168A5033673138 @default.
• W3046717168 hasAuthorship W3046717168A5050306506 @default.
• W3046717168 hasAuthorship W3046717168A5063436972 @default.
• W3046717168 hasAuthorship W3046717168A5063481044 @default.
• W3046717168 hasAuthorship W3046717168A5074141555 @default.
• W3046717168 hasAuthorship W3046717168A5075467680 @default.
• W3046717168 hasAuthorship W3046717168A5087400213 @default.
• W3046717168 hasAuthorship W3046717168A5087648196 @default.
• W3046717168 hasAuthorship W3046717168A5087920560 @default.
• W3046717168 hasBestOaLocation W30467171681 @default.
• W3046717168 hasConcept C107130276 @default.
• W3046717168 hasConcept C108583219 @default.
• W3046717168 hasConcept C119857082 @default.
• W3046717168 hasConcept C126322002 @default.
• W3046717168 hasConcept C126838900 @default.
• W3046717168 hasConcept C138602881 @default.
• W3046717168 hasConcept C154945302 @default.
• W3046717168 hasConcept C187316915 @default.
• W3046717168 hasConcept C194828623 @default.
• W3046717168 hasConcept C2779134260 @default.
• W3046717168 hasConcept C3008058167 @default.
• W3046717168 hasConcept C41008148 @default.
• W3046717168 hasConcept C44249647 @default.
• W3046717168 hasConcept C524204448 @default.
• W3046717168 hasConcept C544519230 @default.
• W3046717168 hasConcept C58471807 @default.
• W3046717168 hasConcept C71924100 @default.
• W3046717168 hasConcept C76318530 @default.
• W3046717168 hasConceptScore W3046717168C107130276 @default.
• W3046717168 hasConceptScore W3046717168C108583219 @default.
• W3046717168 hasConceptScore W3046717168C119857082 @default.
• W3046717168 hasConceptScore W3046717168C126322002 @default.
• W3046717168 hasConceptScore W3046717168C126838900 @default.
• W3046717168 hasConceptScore W3046717168C138602881 @default.

• next