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10.1183/13993003.00775-2020 http://scihub22266oqcxt.onion/10.1183/13993003.00775-2020 32444412!7243395!32444412     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       Eur+Respir+J 2020 ; 56 (2): ä Nephropedia Template TP {{tp|p=32444412|t=2020. A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis |pdf=|usr=}}{{32444412}} gab.com Text A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis JO: Eur Respir J http://www.kidney.de/pget.php?&tw=1&pmid=32444412 #FOAvip Coronavirus disease 2019 (COVID-19) has spread globally, and medical resources become insufficient in many regions. Fast diagnosis of COVID-19 and finding high-risk patients with worse prognosis for early prevention and medical resource optimisation is important. Here, we proposed a fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis by routinely used computed tomography.We retrospectively collected 5372 patients with computed tomography images from seven cities or provinces. Firstly, 4106 patients with computed tomography images were used to pre-train the deep learning system, making it learn lung features. Following this, 1266 patients (924 with COVID-19 (471 had follow-up for >5 days) and 342 with other pneumonia) from six cities or provinces were enrolled to train and externally validate the performance of the deep learning system.In the four external validation sets, the deep learning system achieved good performance in identifying COVID-19 from other pneumonia (AUC 0.87 and 0.88, respectively) and viral pneumonia (AUC 0.86). Moreover, the deep learning system succeeded to stratify patients into high- and low-risk groups whose hospital-stay time had significant difference (p=0.013 and p=0.014, respectively). Without human assistance, the deep learning system automatically focused on abnormal areas that showed consistent characteristics with reported radiological findings.Deep learning provides a convenient tool for fast screening of COVID-19 and identifying potential high-risk patients, which may be helpful for medical resource optimisation and early prevention before patients show severe symptoms. Twit Text FOAVip A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis ????Eur Respir J http://www.kidney.de/pget.php?&tw=1&pmid=32444412 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32444412 #FOAvip English Wikipedia <ref>{{Cite pmid|32444412}}</ref> A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis #MMPMID32444412 Wang S; Zha Y; Li W; Wu Q; Li X; Niu M; Wang M; Qiu X; Li H; Yu H; Gong W; Bai Y; Li L; Zhu Y; Wang L; Tian J Eur Respir J 2020[Aug]; 56 (2): ä PMID32444412show ga Coronavirus disease 2019 (COVID-19) has spread globally, and medical resources become insufficient in many regions. Fast diagnosis of COVID-19 and finding high-risk patients with worse prognosis for early prevention and medical resource optimisation is important. Here, we proposed a fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis by routinely used computed tomography.We retrospectively collected 5372 patients with computed tomography images from seven cities or provinces. Firstly, 4106 patients with computed tomography images were used to pre-train the deep learning system, making it learn lung features. Following this, 1266 patients (924 with COVID-19 (471 had follow-up for >5 days) and 342 with other pneumonia) from six cities or provinces were enrolled to train and externally validate the performance of the deep learning system.In the four external validation sets, the deep learning system achieved good performance in identifying COVID-19 from other pneumonia (AUC 0.87 and 0.88, respectively) and viral pneumonia (AUC 0.86). Moreover, the deep learning system succeeded to stratify patients into high- and low-risk groups whose hospital-stay time had significant difference (p=0.013 and p=0.014, respectively). Without human assistance, the deep learning system automatically focused on abnormal areas that showed consistent characteristics with reported radiological findings.Deep learning provides a convenient tool for fast screening of COVID-19 and identifying potential high-risk patients, which may be helpful for medical resource optimisation and early prevention before patients show severe symptoms. |*Deep Learning[MESH] |Adult[MESH] |Aged[MESH] |Area Under Curve[MESH] |Automation[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |Coronavirus Infections/*diagnostic imaging[MESH] |Female[MESH] |Humans[MESH] |Image Processing, Computer-Assisted/*methods[MESH] |Lung Diseases, Fungal/diagnostic imaging[MESH] |Lung/*diagnostic imaging[MESH] |Male[MESH] |Middle Aged[MESH] |Pandemics[MESH] |Pneumonia, Bacterial/diagnostic imaging[MESH] |Pneumonia, Mycoplasma/diagnostic imaging[MESH] |Pneumonia, Viral/*diagnostic imaging[MESH] |Prognosis[MESH] |Retrospective Studies[MESH] |SARS-CoV-2[MESH]A fully automatic deep learning system for COVID-19 diagnostic and pro(epmc).pdf DeepDyve Pubget Overpricing