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10.1038/s41467-020-18685-1 http://scihub22266oqcxt.onion/10.1038/s41467-020-18685-1 33037212!7547659!33037212     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       Nat+Commun 2020 ; 11 (1): 5088 Nephropedia Template TP {{tp|p=33037212|t=2020. Development and evaluation of an artificial intelligence system for COVID-19 diagnosis |pdf=|usr=}}{{33037212}} gab.com Text Development and evaluation of an artificial intelligence system for COVID-19 diagnosis JO: Nat Commun http://www.kidney.de/pget.php?&tw=1&pmid=33037212 #FOAvip Early detection of COVID-19 based on chest CT enables timely treatment of patients and helps control the spread of the disease. We proposed an artificial intelligence (AI) system for rapid COVID-19 detection and performed extensive statistical analysis of CTs of COVID-19 based on the AI system. We developed and evaluated our system on a large dataset with more than 10 thousand CT volumes from COVID-19, influenza-A/B, non-viral community acquired pneumonia (CAP) and non-pneumonia subjects. In such a difficult multi-class diagnosis task, our deep convolutional neural network-based system is able to achieve an area under the receiver operating characteristic curve (AUC) of 97.81% for multi-way classification on test cohort of 3,199 scans, AUC of 92.99% and 93.25% on two publicly available datasets, CC-CCII and MosMedData respectively. In a reader study involving five radiologists, the AI system outperforms all of radiologists in more challenging tasks at a speed of two orders of magnitude above them. Diagnosis performance of chest x-ray (CXR) is compared to that of CT. Detailed interpretation of deep network is also performed to relate system outputs with CT presentations. The code is available at https://github.com/ChenWWWeixiang/diagnosis_covid19 . Twit Text FOAVip Development and evaluation of an artificial intelligence system for COVID-19 diagnosis ????Nat Commun http://www.kidney.de/pget.php?&tw=1&pmid=33037212 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33037212 #FOAvip English Wikipedia <ref>{{Cite pmid|33037212}}</ref> Development and evaluation of an artificial intelligence system for COVID-19 diagnosis #MMPMID33037212 Jin C; Chen W; Cao Y; Xu Z; Tan Z; Zhang X; Deng L; Zheng C; Zhou J; Shi H; Feng J Nat Commun 2020[Oct]; 11 (1): 5088 PMID33037212show ga Early detection of COVID-19 based on chest CT enables timely treatment of patients and helps control the spread of the disease. We proposed an artificial intelligence (AI) system for rapid COVID-19 detection and performed extensive statistical analysis of CTs of COVID-19 based on the AI system. We developed and evaluated our system on a large dataset with more than 10 thousand CT volumes from COVID-19, influenza-A/B, non-viral community acquired pneumonia (CAP) and non-pneumonia subjects. In such a difficult multi-class diagnosis task, our deep convolutional neural network-based system is able to achieve an area under the receiver operating characteristic curve (AUC) of 97.81% for multi-way classification on test cohort of 3,199 scans, AUC of 92.99% and 93.25% on two publicly available datasets, CC-CCII and MosMedData respectively. In a reader study involving five radiologists, the AI system outperforms all of radiologists in more challenging tasks at a speed of two orders of magnitude above them. Diagnosis performance of chest x-ray (CXR) is compared to that of CT. Detailed interpretation of deep network is also performed to relate system outputs with CT presentations. The code is available at https://github.com/ChenWWWeixiang/diagnosis_covid19 . |*Artificial Intelligence[MESH] |Adult[MESH] |Aged[MESH] |Aged, 80 and over[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |Coronavirus Infections/*diagnostic imaging[MESH] |Deep Learning[MESH] |Diagnosis, Differential[MESH] |Female[MESH] |Humans[MESH] |Male[MESH] |Middle Aged[MESH] |Pandemics[MESH] |Pneumonia, Viral/*diagnostic imaging[MESH] |Pneumonia/diagnostic imaging[MESH] |ROC Curve[MESH] |SARS-CoV-2[MESH] |Tomography, X-Ray Computed[MESH]Development and evaluation of an artificial intelligence system for CO(epmc).pdf DeepDyve Pubget Overpricing