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10.1038/s41467-020-17971-2 http://scihub22266oqcxt.onion/10.1038/s41467-020-17971-2 32796848!7429815!32796848     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 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Nat+Commun 2020 ; 11 (1): 4080 Nephropedia Template TP {{tp|p=32796848|t=2020. Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets |pdf=|usr=}}{{32796848}} gab.com Text Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets JO: Nat Commun http://www.kidney.de/pget.php?&tw=1&pmid=32796848 #FOAvip Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to aid in rapid evaluation of CT scans for differentiation of COVID-19 findings from other clinical entities. Here we show that a series of deep learning algorithms, trained in a diverse multinational cohort of 1280 patients to localize parietal pleura/lung parenchyma followed by classification of COVID-19 pneumonia, can achieve up to 90.8% accuracy, with 84% sensitivity and 93% specificity, as evaluated in an independent test set (not included in training and validation) of 1337 patients. Normal controls included chest CTs from oncology, emergency, and pneumonia-related indications. The false positive rate in 140 patients with laboratory confirmed other (non COVID-19) pneumonias was 10%. AI-based algorithms can readily identify CT scans with COVID-19 associated pneumonia, as well as distinguish non-COVID related pneumonias with high specificity in diverse patient populations. Twit Text FOAVip Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets ????Nat Commun http://www.kidney.de/pget.php?&tw=1&pmid=32796848 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32796848 #FOAvip English Wikipedia <ref>{{Cite pmid|32796848}}</ref> Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets #MMPMID32796848 Harmon SA; Sanford TH; Xu S; Turkbey EB; Roth H; Xu Z; Yang D; Myronenko A; Anderson V; Amalou A; Blain M; Kassin M; Long D; Varble N; Walker SM; Bagci U; Ierardi AM; Stellato E; Plensich GG; Franceschelli G; Girlando C; Irmici G; Labella D; Hammoud D; Malayeri A; Jones E; Summers RM; Choyke PL; Xu D; Flores M; Tamura K; Obinata H; Mori H; Patella F; Cariati M; Carrafiello G; An P; Wood BJ; Turkbey B Nat Commun 2020[Aug]; 11 (1): 4080 PMID32796848show ga Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to aid in rapid evaluation of CT scans for differentiation of COVID-19 findings from other clinical entities. Here we show that a series of deep learning algorithms, trained in a diverse multinational cohort of 1280 patients to localize parietal pleura/lung parenchyma followed by classification of COVID-19 pneumonia, can achieve up to 90.8% accuracy, with 84% sensitivity and 93% specificity, as evaluated in an independent test set (not included in training and validation) of 1337 patients. Normal controls included chest CTs from oncology, emergency, and pneumonia-related indications. The false positive rate in 140 patients with laboratory confirmed other (non COVID-19) pneumonias was 10%. AI-based algorithms can readily identify CT scans with COVID-19 associated pneumonia, as well as distinguish non-COVID related pneumonias with high specificity in diverse patient populations. |*Artificial Intelligence[MESH] |Adolescent[MESH] |Adult[MESH] |Aged[MESH] |Aged, 80 and over[MESH] |Algorithms[MESH] |Betacoronavirus/isolation & purification[MESH] |COVID-19[MESH] |COVID-19 Testing[MESH] |Child[MESH] |Child, Preschool[MESH] |Clinical Laboratory Techniques/*methods[MESH] |Coronavirus Infections/diagnosis/*diagnostic imaging/virology[MESH] |Deep Learning[MESH] |Female[MESH] |Humans[MESH] |Imaging, Three-Dimensional/methods[MESH] |Lung/diagnostic imaging[MESH] |Male[MESH] |Middle Aged[MESH] |Pandemics[MESH] |Pneumonia, Viral/*diagnostic imaging/virology[MESH] |Radiographic Image Interpretation, Computer-Assisted/methods[MESH] |SARS-CoV-2[MESH] |Tomography, X-Ray Computed/*methods[MESH]Artificial intelligence for the detection of COVID-19 pneumonia on che(epmc).pdf DeepDyve Pubget Overpricing