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10.1073/pnas.2019893118 http://scihub22266oqcxt.onion/10.1073/pnas.2019893118 33674422!7999948!33674422     free     free     free Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Proc+Natl+Acad+Sci+U+S+A 2021 ; 118 (12): ä Nephropedia Template TP {{tp|p=33674422|t=2021. Using artificial intelligence to improve COVID-19 rapid diagnostic test result interpretation |pdf=|usr=}}{{33674422}} gab.com Text Using artificial intelligence to improve COVID-19 rapid diagnostic test result interpretation JO: Proc Natl Acad Sci U S A http://www.kidney.de/pget.php?&tw=1&pmid=33674422 #FOAvip Serological rapid diagnostic tests (RDTs) are widely used across pathologies, often providing users a simple, binary result (positive or negative) in as little as 5 to 20 min. Since the beginning of the COVID-19 pandemic, new RDTs for identifying SARS-CoV-2 have rapidly proliferated. However, these seemingly easy-to-read tests can be highly subjective, and interpretations of the visible "bands" of color that appear (or not) in a test window may vary between users, test models, and brands. We developed and evaluated the accuracy/performance of a smartphone application (xRCovid) that uses machine learning to classify SARS-CoV-2 serological RDT results and reduce reading ambiguities. Across 11 COVID-19 RDT models, the app yielded 99.3% precision compared to reading by eye. Using the app replaces the uncertainty from visual RDT interpretation with a smaller uncertainty of the image classifier, thereby increasing confidence of clinicians and laboratory staff when using RDTs, and creating opportunities for patient self-testing. Twit Text FOAVip Using artificial intelligence to improve COVID-19 rapid diagnostic test result interpretation ????Proc Natl Acad Sci U S A http://www.kidney.de/pget.php?&tw=1&pmid=33674422 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33674422 #FOAvip English Wikipedia <ref>{{Cite pmid|33674422}}</ref> Using artificial intelligence to improve COVID-19 rapid diagnostic test result interpretation #MMPMID33674422 Mendels DA; Dortet L; Emeraud C; Oueslati S; Girlich D; Ronat JB; Bernabeu S; Bahi S; Atkinson GJH; Naas T Proc Natl Acad Sci U S A 2021[Mar]; 118 (12): ä PMID33674422show ga Serological rapid diagnostic tests (RDTs) are widely used across pathologies, often providing users a simple, binary result (positive or negative) in as little as 5 to 20 min. Since the beginning of the COVID-19 pandemic, new RDTs for identifying SARS-CoV-2 have rapidly proliferated. However, these seemingly easy-to-read tests can be highly subjective, and interpretations of the visible "bands" of color that appear (or not) in a test window may vary between users, test models, and brands. We developed and evaluated the accuracy/performance of a smartphone application (xRCovid) that uses machine learning to classify SARS-CoV-2 serological RDT results and reduce reading ambiguities. Across 11 COVID-19 RDT models, the app yielded 99.3% precision compared to reading by eye. Using the app replaces the uncertainty from visual RDT interpretation with a smaller uncertainty of the image classifier, thereby increasing confidence of clinicians and laboratory staff when using RDTs, and creating opportunities for patient self-testing. |*COVID-19 Serological Testing[MESH] |*Machine Learning[MESH] |*Mobile Applications[MESH] |*SARS-CoV-2[MESH] |COVID-19/*diagnosis[MESH]Using artificial intelligence to improve COVID-19 rapid diagnostic tes(epmc).pdf DeepDyve Pubget Overpricing