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10.1016/j.asoc.2020.106897 http://scihub22266oqcxt.onion/10.1016/j.asoc.2020.106897 33199977!7654325!33199977     free     free     free Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Appl+Soft+Comput 2021 ; 98 (ä): 106897 Nephropedia Template TP {{tp|p=33199977|t=2021. AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system |pdf=|usr=}}{{33199977}} gab.com Text AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system JO: Appl Soft Comput http://www.kidney.de/pget.php?&tw=1&pmid=33199977 #FOAvip The sudden outbreak of novel coronavirus 2019 (COVID-19) increased the diagnostic burden of radiologists. In the time of an epidemic crisis, we hope artificial intelligence (AI) to reduce physician workload in regions with the outbreak, and improve the diagnosis accuracy for physicians before they could acquire enough experience with the new disease. In this paper, we present our experience in building and deploying an AI system that automatically analyzes CT images and provides the probability of infection to rapidly detect COVID-19 pneumonia. The proposed system which consists of classification and segmentation will save about 30%-40% of the detection time for physicians and promote the performance of COVID-19 detection. Specifically, working in an interdisciplinary team of over 30 people with medical and/or AI background, geographically distributed in Beijing and Wuhan, we are able to overcome a series of challenges (e.g. data discrepancy, testing time-effectiveness of model, data security, etc.) in this particular situation and deploy the system in four weeks. In addition, since the proposed AI system provides the priority of each CT image with probability of infection, the physicians can confirm and segregate the infected patients in time. Using 1,136 training cases (723 positives for COVID-19) from five hospitals, we are able to achieve a sensitivity of 0.974 and specificity of 0.922 on the test dataset, which included a variety of pulmonary diseases. Twit Text FOAVip AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system ????Appl Soft Comput http://www.kidney.de/pget.php?&tw=1&pmid=33199977 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33199977 #FOAvip English Wikipedia <ref>{{Cite pmid|33199977}}</ref> AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system #MMPMID33199977 Wang B; Jin S; Yan Q; Xu H; Luo C; Wei L; Zhao W; Hou X; Ma W; Xu Z; Zheng Z; Sun W; Lan L; Zhang W; Mu X; Shi C; Wang Z; Lee J; Jin Z; Lin M; Jin H; Zhang L; Guo J; Zhao B; Ren Z; Wang S; Xu W; Wang X; Wang J; You Z; Dong J Appl Soft Comput 2021[Jan]; 98 (ä): 106897 PMID33199977show ga The sudden outbreak of novel coronavirus 2019 (COVID-19) increased the diagnostic burden of radiologists. In the time of an epidemic crisis, we hope artificial intelligence (AI) to reduce physician workload in regions with the outbreak, and improve the diagnosis accuracy for physicians before they could acquire enough experience with the new disease. In this paper, we present our experience in building and deploying an AI system that automatically analyzes CT images and provides the probability of infection to rapidly detect COVID-19 pneumonia. The proposed system which consists of classification and segmentation will save about 30%-40% of the detection time for physicians and promote the performance of COVID-19 detection. Specifically, working in an interdisciplinary team of over 30 people with medical and/or AI background, geographically distributed in Beijing and Wuhan, we are able to overcome a series of challenges (e.g. data discrepancy, testing time-effectiveness of model, data security, etc.) in this particular situation and deploy the system in four weeks. In addition, since the proposed AI system provides the priority of each CT image with probability of infection, the physicians can confirm and segregate the infected patients in time. Using 1,136 training cases (723 positives for COVID-19) from five hospitals, we are able to achieve a sensitivity of 0.974 and specificity of 0.922 on the test dataset, which included a variety of pulmonary diseases. äAI-assisted CT imaging analysis for COVID-19 screening: Building and d(epmc).pdf DeepDyve Pubget Overpricing