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10.1038/s41598-020-76282-0 http://scihub22266oqcxt.onion/10.1038/s41598-020-76282-0 33154542!7645624!33154542     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 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Sci+Rep 2020 ; 10 (1): 19196 Nephropedia Template TP {{tp|p=33154542|t=2020. Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography |pdf=|usr=}}{{33154542}} gab.com Text Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=33154542 #FOAvip Computed tomography (CT) is the preferred imaging method for diagnosing 2019 novel coronavirus (COVID19) pneumonia. We aimed to construct a system based on deep learning for detecting COVID-19 pneumonia on high resolution CT. For model development and validation, 46,096 anonymous images from 106 admitted patients, including 51 patients of laboratory confirmed COVID-19 pneumonia and 55 control patients of other diseases in Renmin Hospital of Wuhan University were retrospectively collected. Twenty-seven prospective consecutive patients in Renmin Hospital of Wuhan University were collected to evaluate the efficiency of radiologists against 2019-CoV pneumonia with that of the model. An external test was conducted in Qianjiang Central Hospital to estimate the system's robustness. The model achieved a per-patient accuracy of 95.24% and a per-image accuracy of 98.85% in internal retrospective dataset. For 27 internal prospective patients, the system achieved a comparable performance to that of expert radiologist. In external dataset, it achieved an accuracy of 96%. With the assistance of the model, the reading time of radiologists was greatly decreased by 65%. The deep learning model showed a comparable performance with expert radiologist, and greatly improved the efficiency of radiologists in clinical practice. Twit Text FOAVip Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=33154542 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33154542 #FOAvip English Wikipedia <ref>{{Cite pmid|33154542}}</ref> Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography #MMPMID33154542 Chen J; Wu L; Zhang J; Zhang L; Gong D; Zhao Y; Chen Q; Huang S; Yang M; Yang X; Hu S; Wang Y; Hu X; Zheng B; Zhang K; Wu H; Dong Z; Xu Y; Zhu Y; Chen X; Zhang M; Yu L; Cheng F; Yu H Sci Rep 2020[Nov]; 10 (1): 19196 PMID33154542show ga Computed tomography (CT) is the preferred imaging method for diagnosing 2019 novel coronavirus (COVID19) pneumonia. We aimed to construct a system based on deep learning for detecting COVID-19 pneumonia on high resolution CT. For model development and validation, 46,096 anonymous images from 106 admitted patients, including 51 patients of laboratory confirmed COVID-19 pneumonia and 55 control patients of other diseases in Renmin Hospital of Wuhan University were retrospectively collected. Twenty-seven prospective consecutive patients in Renmin Hospital of Wuhan University were collected to evaluate the efficiency of radiologists against 2019-CoV pneumonia with that of the model. An external test was conducted in Qianjiang Central Hospital to estimate the system's robustness. The model achieved a per-patient accuracy of 95.24% and a per-image accuracy of 98.85% in internal retrospective dataset. For 27 internal prospective patients, the system achieved a comparable performance to that of expert radiologist. In external dataset, it achieved an accuracy of 96%. With the assistance of the model, the reading time of radiologists was greatly decreased by 65%. The deep learning model showed a comparable performance with expert radiologist, and greatly improved the efficiency of radiologists in clinical practice. |*Deep Learning[MESH] |*Signal-To-Noise Ratio[MESH] |*Tomography, X-Ray Computed[MESH] |Adult[MESH] |COVID-19[MESH] |Coronavirus Infections/*complications[MESH] |Female[MESH] |Humans[MESH] |Image Processing, Computer-Assisted/*methods[MESH] |Male[MESH] |Middle Aged[MESH] |Pandemics[MESH] |Pneumonia, Viral/*complications[MESH] |Pneumonia/*complications/*diagnostic imaging[MESH]Deep learning-based model for detecting 2019 novel coronavirus pneumon(epmc).pdf DeepDyve Pubget Overpricing