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.

10.1038/s41598-020-80261-w http://scihub22266oqcxt.onion/10.1038/s41598-020-80261-w 33432072!7801482!33432072     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Sci+Rep 2021 ; 11 (1): 417 Nephropedia Template TP {{tp|p=33432072|t=2021. A novel deep learning-based quantification of serial chest computed tomography in Coronavirus Disease 2019 (COVID-19) |pdf=|usr=}}{{33432072}} gab.com Text A novel deep learning-based quantification of serial chest computed tomography in Coronavirus Disease 2019 (COVID-19) JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=33432072 #FOAvip This study aims to explore and compare a novel deep learning-based quantification with the conventional semi-quantitative computed tomography (CT) scoring for the serial chest CT scans of COVID-19. 95 patients with confirmed COVID-19 and a total of 465 serial chest CT scans were involved, including 61 moderate patients (moderate group, 319 chest CT scans) and 34 severe patients (severe group, 146 chest CT scans). Conventional CT scoring and deep learning-based quantification were performed for all chest CT scans for two study goals: (1) Correlation between these two estimations; (2) Exploring the dynamic patterns using these two estimations between moderate and severe groups. The Spearman's correlation coefficient between these two estimation methods was 0.920 (p < 0.001). predicted pulmonary involvement (CT score and percent of pulmonary lesions calculated using deep learning-based quantification) increased more rapidly and reached a higher peak on 23rd days from symptom onset in severe group, which reached a peak on 18th days in moderate group with faster absorption of the lesions. The deep learning-based quantification for COVID-19 showed a good correlation with the conventional CT scoring and demonstrated a potential benefit in the estimation of disease severities of COVID-19. Twit Text FOAVip A novel deep learning-based quantification of serial chest computed tomography in Coronavirus Disease 2019 (COVID-19) ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=33432072 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33432072 #FOAvip English Wikipedia <ref>{{Cite pmid|33432072}}</ref> A novel deep learning-based quantification of serial chest computed tomography in Coronavirus Disease 2019 (COVID-19) #MMPMID33432072 Pan F; Li L; Liu B; Ye T; Li L; Liu D; Ding Z; Chen G; Liang B; Yang L; Zheng C Sci Rep 2021[Jan]; 11 (1): 417 PMID33432072show ga This study aims to explore and compare a novel deep learning-based quantification with the conventional semi-quantitative computed tomography (CT) scoring for the serial chest CT scans of COVID-19. 95 patients with confirmed COVID-19 and a total of 465 serial chest CT scans were involved, including 61 moderate patients (moderate group, 319 chest CT scans) and 34 severe patients (severe group, 146 chest CT scans). Conventional CT scoring and deep learning-based quantification were performed for all chest CT scans for two study goals: (1) Correlation between these two estimations; (2) Exploring the dynamic patterns using these two estimations between moderate and severe groups. The Spearman's correlation coefficient between these two estimation methods was 0.920 (p < 0.001). predicted pulmonary involvement (CT score and percent of pulmonary lesions calculated using deep learning-based quantification) increased more rapidly and reached a higher peak on 23rd days from symptom onset in severe group, which reached a peak on 18th days in moderate group with faster absorption of the lesions. The deep learning-based quantification for COVID-19 showed a good correlation with the conventional CT scoring and demonstrated a potential benefit in the estimation of disease severities of COVID-19. |*Deep Learning[MESH] |*Tomography, X-Ray Computed/methods[MESH] |Adult[MESH] |Aged[MESH] |COVID-19/*diagnostic imaging[MESH] |Female[MESH] |Humans[MESH] |Lung/*diagnostic imaging[MESH] |Male[MESH] |Middle Aged[MESH] |Retrospective Studies[MESH] |SARS-CoV-2/isolation & purification[MESH]A novel deep learning-based quantification of serial chest computed to(epmc).pdf DeepDyve Pubget Overpricing