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10.1007/s10140-020-01856-4 http://scihub22266oqcxt.onion/10.1007/s10140-020-01856-4 33037946!7547301!33037946     free     free     free 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 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Emerg+Radiol 2020 ; 27 (6): 671-678 Nephropedia Template TP {{tp|p=33037946|t=2020. Dynamic evaluation of lung involvement during coronavirus disease-2019 (COVID-19) with quantitative lung CT |pdf=|usr=}}{{33037946}} gab.com Text Dynamic evaluation of lung involvement during coronavirus disease-2019 (COVID-19) with quantitative lung CT JO: Emerg Radiol http://www.kidney.de/pget.php?&tw=1&pmid=33037946 #FOAvip PURPOSE: To identify and quantify lung changes associated with coronavirus disease-2019 (COVID-19) with quantitative lung CT during the disease. METHODS: This retrospective study reviewed COVID-19 patients who underwent multiple chest CT scans during their disease course. Quantitative lung CT was used to determine the nature and volume of lung involvement. A semi-quantitative scoring system was also used to evaluate lung lesions. RESULTS: This study included eighteen cases (4 cases in mild type, 10 cases in moderate type, 4 cases in severe type, and without critical type cases) with confirmed COVID-19. Patients had a mean hospitalized period of 24.1 +/- 7.1 days (range: 14-38 days) and underwent an average CT scans of 3.9 +/- 1.6 (range: 2-8). The total volumes of lung abnormalities reached a peak of 8.8 +/- 4.1 days (range: 2-14 days). The ground-glass opacity (GGO) volume percentage was higher than the consolidative opacity (CO) volume percentage on the first CT examination (Z = 2.229, P = 0.026), and there was no significant difference between the GGO volume percentage and that of CO at the peak stage (Z = - 0.628, P = 0.53). The volume percentage of lung involvement identified by AI demonstrated a strong correlation with the total CT scores at each stage (r = 0.873, P = 0.0001). CONCLUSIONS: Quantitative lung CT can automatically identify the nature of lung involvement and quantify the dynamic changes of lung lesions on CT during COVID-19. For patients who recovered from COVID-19, GGO was the predominant imaging feature on the initial CT scan, while GGO and CO were the main appearances at peak stage. Twit Text FOAVip Dynamic evaluation of lung involvement during coronavirus disease-2019 (COVID-19) with quantitative lung CT ????Emerg Radiol http://www.kidney.de/pget.php?&tw=1&pmid=33037946 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33037946 #FOAvip English Wikipedia <ref>{{Cite pmid|33037946}}</ref> Dynamic evaluation of lung involvement during coronavirus disease-2019 (COVID-19) with quantitative lung CT #MMPMID33037946 Ma C; Wang XL; Xie DM; Li YD; Zheng YJ; Zhang HB; Ming B Emerg Radiol 2020[Dec]; 27 (6): 671-678 PMID33037946show ga PURPOSE: To identify and quantify lung changes associated with coronavirus disease-2019 (COVID-19) with quantitative lung CT during the disease. METHODS: This retrospective study reviewed COVID-19 patients who underwent multiple chest CT scans during their disease course. Quantitative lung CT was used to determine the nature and volume of lung involvement. A semi-quantitative scoring system was also used to evaluate lung lesions. RESULTS: This study included eighteen cases (4 cases in mild type, 10 cases in moderate type, 4 cases in severe type, and without critical type cases) with confirmed COVID-19. Patients had a mean hospitalized period of 24.1 +/- 7.1 days (range: 14-38 days) and underwent an average CT scans of 3.9 +/- 1.6 (range: 2-8). The total volumes of lung abnormalities reached a peak of 8.8 +/- 4.1 days (range: 2-14 days). The ground-glass opacity (GGO) volume percentage was higher than the consolidative opacity (CO) volume percentage on the first CT examination (Z = 2.229, P = 0.026), and there was no significant difference between the GGO volume percentage and that of CO at the peak stage (Z = - 0.628, P = 0.53). The volume percentage of lung involvement identified by AI demonstrated a strong correlation with the total CT scores at each stage (r = 0.873, P = 0.0001). CONCLUSIONS: Quantitative lung CT can automatically identify the nature of lung involvement and quantify the dynamic changes of lung lesions on CT during COVID-19. For patients who recovered from COVID-19, GGO was the predominant imaging feature on the initial CT scan, while GGO and CO were the main appearances at peak stage. |Adolescent[MESH] |Adult[MESH] |Aged[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |Child[MESH] |Child, Preschool[MESH] |Coronavirus Infections/*diagnostic imaging[MESH] |Female[MESH] |Humans[MESH] |Infant[MESH] |Male[MESH] |Middle Aged[MESH] |Pandemics[MESH] |Pneumonia, Viral/*diagnostic imaging[MESH] |Retrospective Studies[MESH] |SARS-CoV-2[MESH]Dynamic evaluation of lung involvement during coronavirus disease-2019(epmc).pdf DeepDyve Pubget Overpricing