Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1109/JBHI.2020.3036722 http://scihub22266oqcxt.onion/10.1109/JBHI.2020.3036722 33166256!8545168!33166256     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33166256&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       IEEE+J+Biomed+Health+Inform 2020 ; 24 (12): 3585-3594 Nephropedia Template TP {{tp|p=33166256|t=2020. Classification of Severe and Critical Covid-19 Using Deep Learning and Radiomics |pdf=|usr=}}{{33166256}} gab.com Text Classification of Severe and Critical Covid-19 Using Deep Learning and Radiomics JO: IEEE J Biomed Health Inform http://www.kidney.de/pget.php?&tw=1&pmid=33166256 #FOAvip OBJECTIVE: The coronavirus disease 2019 (COVID-19) is rapidly spreading inside China and internationally. We aimed to construct a model integrating information from radiomics and deep learning (DL) features to discriminate critical cases from severe cases of COVID-19 using computed tomography (CT) images. METHODS: We retrospectively enrolled 217 patients from three centers in China, including 82 patients with severe disease and 135 with critical disease. Patients were randomly divided into a training cohort (n = 174) and a test cohort (n = 43). We extracted 102 3-dimensional radiomic features from automatically segmented lung volume and selected the significant features. We also developed a 3-dimensional DL network based on center-cropped slices. Using multivariable logistic regression, we then created a merged model based on significant radiomic features and DL scores. We employed the area under the receiver operating characteristic curve (AUC) to evaluate the model's performance. We then conducted cross validation, stratified analysis, survival analysis, and decision curve analysis to evaluate the robustness of our method. RESULTS: The merged model can distinguish critical patients with AUCs of 0.909 (95% confidence interval [CI]: 0.859-0.952) and 0.861 (95% CI: 0.753-0.968) in the training and test cohorts, respectively. Stratified analysis indicated that our model was not affected by sex, age, or chronic disease. Moreover, the results of the merged model showed a strong correlation with patient outcomes. SIGNIFICANCE: A model combining radiomic and DL features of the lung could help distinguish critical cases from severe cases of COVID-19. Twit Text FOAVip Classification of Severe and Critical Covid-19 Using Deep Learning and Radiomics ????IEEE J Biomed Health Inform http://www.kidney.de/pget.php?&tw=1&pmid=33166256 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33166256 #FOAvip English Wikipedia <ref>{{Cite pmid|33166256}}</ref> Classification of Severe and Critical Covid-19 Using Deep Learning and Radiomics #MMPMID33166256 Li C; Dong D; Li L; Gong W; Li X; Bai Y; Wang M; Hu Z; Zha Y; Tian J IEEE J Biomed Health Inform 2020[Dec]; 24 (12): 3585-3594 PMID33166256show ga OBJECTIVE: The coronavirus disease 2019 (COVID-19) is rapidly spreading inside China and internationally. We aimed to construct a model integrating information from radiomics and deep learning (DL) features to discriminate critical cases from severe cases of COVID-19 using computed tomography (CT) images. METHODS: We retrospectively enrolled 217 patients from three centers in China, including 82 patients with severe disease and 135 with critical disease. Patients were randomly divided into a training cohort (n = 174) and a test cohort (n = 43). We extracted 102 3-dimensional radiomic features from automatically segmented lung volume and selected the significant features. We also developed a 3-dimensional DL network based on center-cropped slices. Using multivariable logistic regression, we then created a merged model based on significant radiomic features and DL scores. We employed the area under the receiver operating characteristic curve (AUC) to evaluate the model's performance. We then conducted cross validation, stratified analysis, survival analysis, and decision curve analysis to evaluate the robustness of our method. RESULTS: The merged model can distinguish critical patients with AUCs of 0.909 (95% confidence interval [CI]: 0.859-0.952) and 0.861 (95% CI: 0.753-0.968) in the training and test cohorts, respectively. Stratified analysis indicated that our model was not affected by sex, age, or chronic disease. Moreover, the results of the merged model showed a strong correlation with patient outcomes. SIGNIFICANCE: A model combining radiomic and DL features of the lung could help distinguish critical cases from severe cases of COVID-19. |COVID-19/diagnostic imaging/*physiopathology/virology[MESH] |Cohort Studies[MESH] |Female[MESH] |Humans[MESH] |Male[MESH] |Middle Aged[MESH] |SARS-CoV-2/isolation & purification[MESH] |Severity of Illness Index[MESH]Classification of Severe and Critical Covid-19 Using Deep Learning and(epmc).pdf DeepDyve Pubget Overpricing