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10.1038/s41598-021-83237-6 http://scihub22266oqcxt.onion/10.1038/s41598-021-83237-6 33594159!7886892!33594159     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33594159&cmd=llinks): Failed to open stream: HTTP request failed! 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A deep learning integrated radiomics model for identification of coronavirus disease 2019 using computed tomography |pdf=|usr=}}{{33594159}} gab.com Text A deep learning integrated radiomics model for identification of coronavirus disease 2019 using computed tomography JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=33594159 #FOAvip Since its first outbreak, Coronavirus Disease 2019 (COVID-19) has been rapidly spreading worldwide and caused a global pandemic. Rapid and early detection is essential to contain COVID-19. Here, we first developed a deep learning (DL) integrated radiomics model for end-to-end identification of COVID-19 using CT scans and then validated its clinical feasibility. We retrospectively collected CT images of 386 patients (129 with COVID-19 and 257 with other community-acquired pneumonia) from three medical centers to train and externally validate the developed models. A pre-trained DL algorithm was utilized to automatically segment infected lesions (ROIs) on CT images which were used for feature extraction. Five feature selection methods and four machine learning algorithms were utilized to develop radiomics models. Trained with features selected by L1 regularized logistic regression, classifier multi-layer perceptron (MLP) demonstrated the optimal performance with AUC of 0.922 (95% CI 0.856-0.988) and 0.959 (95% CI 0.910-1.000), the same sensitivity of 0.879, and specificity of 0.900 and 0.887 on internal and external testing datasets, which was equivalent to the senior radiologist in a reader study. Additionally, diagnostic time of DL-MLP was more efficient than radiologists (38 s vs 5.15 min). With an adequate performance for identifying COVID-19, DL-MLP may help in screening of suspected cases. Twit Text FOAVip A deep learning integrated radiomics model for identification of coronavirus disease 2019 using computed tomography ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=33594159 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33594159 #FOAvip English Wikipedia <ref>{{Cite pmid|33594159}}</ref> A deep learning integrated radiomics model for identification of coronavirus disease 2019 using computed tomography #MMPMID33594159 Zhang X; Wang D; Shao J; Tian S; Tan W; Ma Y; Xu Q; Ma X; Li D; Chai J; Wang D; Liu W; Lin L; Wu J; Xia C; Zhang Z Sci Rep 2021[Feb]; 11 (1): 3938 PMID33594159show ga Since its first outbreak, Coronavirus Disease 2019 (COVID-19) has been rapidly spreading worldwide and caused a global pandemic. Rapid and early detection is essential to contain COVID-19. Here, we first developed a deep learning (DL) integrated radiomics model for end-to-end identification of COVID-19 using CT scans and then validated its clinical feasibility. We retrospectively collected CT images of 386 patients (129 with COVID-19 and 257 with other community-acquired pneumonia) from three medical centers to train and externally validate the developed models. A pre-trained DL algorithm was utilized to automatically segment infected lesions (ROIs) on CT images which were used for feature extraction. Five feature selection methods and four machine learning algorithms were utilized to develop radiomics models. Trained with features selected by L1 regularized logistic regression, classifier multi-layer perceptron (MLP) demonstrated the optimal performance with AUC of 0.922 (95% CI 0.856-0.988) and 0.959 (95% CI 0.910-1.000), the same sensitivity of 0.879, and specificity of 0.900 and 0.887 on internal and external testing datasets, which was equivalent to the senior radiologist in a reader study. Additionally, diagnostic time of DL-MLP was more efficient than radiologists (38 s vs 5.15 min). With an adequate performance for identifying COVID-19, DL-MLP may help in screening of suspected cases. |*Deep Learning[MESH] |*Models, Biological[MESH] |*Tomography, X-Ray Computed[MESH] |Adult[MESH] |Algorithms[MESH] |COVID-19/*diagnostic imaging/*virology[MESH] |Female[MESH] |Humans[MESH] |Male[MESH] |Middle Aged[MESH] |ROC Curve[MESH] |Radiologists[MESH]A deep learning integrated radiomics model for identification of coron(epmc).pdf DeepDyve Pubget Overpricing