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10.1088/1361-6560/abbf9e http://scihub22266oqcxt.onion/10.1088/1361-6560/abbf9e 33032267!ä!33032267 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Phys+Med+Biol 2021 ; 66 (3): 035015 Nephropedia Template TP {{tp|p=33032267|t=2021. Severity assessment of COVID-19 using CT image features and laboratory indices |pdf=|usr=}}{{33032267}} gab.com Text Severity assessment of COVID-19 using CT image features and laboratory indices JO: Phys Med Biol http://www.kidney.de/pget.php?&tw=1&pmid=33032267 #FOAvip The coronavirus disease 2019 (COVID-19) is now a global pandemic. Tens of millions of people have been confirmed with infection, and also more people are suspected. Chest computed tomography (CT) is recognized as an important tool for COVID-19 severity assessment. As the number of chest CT images increases rapidly, manual severity assessment becomes a labor-intensive task, delaying appropriate isolation and treatment. In this paper, a study of automatic severity assessment for COVID-19 is presented. Specifically, chest CT images of 118 patients (age 46.5 +/- 16.5 years, 64 male and 54 female) with confirmed COVID-19 infection are used, from which 63 quantitative features and 110 radiomics features are derived. Besides the chest CT image features, 36 laboratory indices of each patient are also used, which can provide complementary information from a different view. A random forest (RF) model is trained to assess the severity (non-severe or severe) according to the chest CT image features and laboratory indices. Importance of each chest CT image feature and laboratory index, which reflects the correlation to the severity of COVID-19, is also calculated from the RF model. Using three-fold cross-validation, the RF model shows promising results: 0.910 (true positive ratio), 0.858 (true negative ratio) and 0.890 (accuracy), along with AUC of 0.98. Moreover, several chest CT image features and laboratory indices are found to be highly related to COVID-19 severity, which could be valuable for the clinical diagnosis of COVID-19. Twit Text FOAVip Severity assessment of COVID-19 using CT image features and laboratory indices ????Phys Med Biol http://www.kidney.de/pget.php?&tw=1&pmid=33032267 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33032267 #FOAvip English Wikipedia <ref>{{Cite pmid|33032267}}</ref> Severity assessment of COVID-19 using CT image features and laboratory indices #MMPMID33032267 Tang Z; Zhao W; Xie X; Zhong Z; Shi F; Ma T; Liu J; Shen D Phys Med Biol 2021[Jan]; 66 (3): 035015 PMID33032267show ga The coronavirus disease 2019 (COVID-19) is now a global pandemic. Tens of millions of people have been confirmed with infection, and also more people are suspected. Chest computed tomography (CT) is recognized as an important tool for COVID-19 severity assessment. As the number of chest CT images increases rapidly, manual severity assessment becomes a labor-intensive task, delaying appropriate isolation and treatment. In this paper, a study of automatic severity assessment for COVID-19 is presented. Specifically, chest CT images of 118 patients (age 46.5 +/- 16.5 years, 64 male and 54 female) with confirmed COVID-19 infection are used, from which 63 quantitative features and 110 radiomics features are derived. Besides the chest CT image features, 36 laboratory indices of each patient are also used, which can provide complementary information from a different view. A random forest (RF) model is trained to assess the severity (non-severe or severe) according to the chest CT image features and laboratory indices. Importance of each chest CT image feature and laboratory index, which reflects the correlation to the severity of COVID-19, is also calculated from the RF model. Using three-fold cross-validation, the RF model shows promising results: 0.910 (true positive ratio), 0.858 (true negative ratio) and 0.890 (accuracy), along with AUC of 0.98. Moreover, several chest CT image features and laboratory indices are found to be highly related to COVID-19 severity, which could be valuable for the clinical diagnosis of COVID-19. |*Radiography, Thoracic[MESH] |*Tomography, X-Ray Computed[MESH] |Adult[MESH] |Area Under Curve[MESH] |COVID-19/*diagnostic imaging[MESH] |False Positive Reactions[MESH] |Female[MESH] |Humans[MESH] |Laboratories[MESH] |Lung/diagnostic imaging[MESH] |Male[MESH] |Middle Aged[MESH] |Pandemics[MESH] |Retrospective Studies[MESH]Severity assessment of COVID-19 using CT image features and laboratory(epmc).pdf DeepDyve Pubget Overpricing