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10.1148/radiol.2020201874 http://scihub22266oqcxt.onion/10.1148/radiol.2020201874 32384019!7437494!32384019     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 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       Radiology 2020 ; 296 (3): E166-E172 Nephropedia Template TP {{tp|p=32384019|t=2020. COVID-19 on Chest Radiographs: A Multireader Evaluation of an Artificial Intelligence System |pdf=|usr=}}{{32384019}} gab.com Text COVID-19 on Chest Radiographs: A Multireader Evaluation of an Artificial Intelligence System JO: Radiology http://www.kidney.de/pget.php?&tw=1&pmid=32384019 #FOAvip Background Chest radiography may play an important role in triage for coronavirus disease 2019 (COVID-19), particularly in low-resource settings. Purpose To evaluate the performance of an artificial intelligence (AI) system for detection of COVID-19 pneumonia on chest radiographs. Materials and Methods An AI system (CAD4COVID-XRay) was trained on 24 678 chest radiographs, including 1540 used only for validation while training. The test set consisted of a set of continuously acquired chest radiographs (n = 454) obtained in patients suspected of having COVID-19 pneumonia between March 4 and April 6, 2020, at one center (223 patients with positive reverse transcription polymerase chain reaction [RT-PCR] results, 231 with negative RT-PCR results). Radiographs were independently analyzed by six readers and by the AI system. Diagnostic performance was analyzed with the receiver operating characteristic curve. Results For the test set, the mean age of patients was 67 years +/- 14.4 (standard deviation) (56% male). With RT-PCR test results as the reference standard, the AI system correctly classified chest radiographs as COVID-19 pneumonia with an area under the receiver operating characteristic curve of 0.81. The system significantly outperformed each reader (P < .001 using the McNemar test) at their highest possible sensitivities. At their lowest sensitivities, only one reader significantly outperformed the AI system (P = .04). Conclusion The performance of an artificial intelligence system in the detection of coronavirus disease 2019 on chest radiographs was comparable with that of six independent readers. (c) RSNA, 2020. Twit Text FOAVip COVID-19 on Chest Radiographs: A Multireader Evaluation of an Artificial Intelligence System ????Radiology http://www.kidney.de/pget.php?&tw=1&pmid=32384019 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32384019 #FOAvip English Wikipedia <ref>{{Cite pmid|32384019}}</ref> COVID-19 on Chest Radiographs: A Multireader Evaluation of an Artificial Intelligence System #MMPMID32384019 Murphy K; Smits H; Knoops AJG; Korst MBJM; Samson T; Scholten ET; Schalekamp S; Schaefer-Prokop CM; Philipsen RHHM; Meijers A; Melendez J; van Ginneken B; Rutten M Radiology 2020[Sep]; 296 (3): E166-E172 PMID32384019show ga Background Chest radiography may play an important role in triage for coronavirus disease 2019 (COVID-19), particularly in low-resource settings. Purpose To evaluate the performance of an artificial intelligence (AI) system for detection of COVID-19 pneumonia on chest radiographs. Materials and Methods An AI system (CAD4COVID-XRay) was trained on 24 678 chest radiographs, including 1540 used only for validation while training. The test set consisted of a set of continuously acquired chest radiographs (n = 454) obtained in patients suspected of having COVID-19 pneumonia between March 4 and April 6, 2020, at one center (223 patients with positive reverse transcription polymerase chain reaction [RT-PCR] results, 231 with negative RT-PCR results). Radiographs were independently analyzed by six readers and by the AI system. Diagnostic performance was analyzed with the receiver operating characteristic curve. Results For the test set, the mean age of patients was 67 years +/- 14.4 (standard deviation) (56% male). With RT-PCR test results as the reference standard, the AI system correctly classified chest radiographs as COVID-19 pneumonia with an area under the receiver operating characteristic curve of 0.81. The system significantly outperformed each reader (P < .001 using the McNemar test) at their highest possible sensitivities. At their lowest sensitivities, only one reader significantly outperformed the AI system (P = .04). Conclusion The performance of an artificial intelligence system in the detection of coronavirus disease 2019 on chest radiographs was comparable with that of six independent readers. (c) RSNA, 2020. |*Artificial Intelligence[MESH] |Aged[MESH] |Aged, 80 and over[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |Coronavirus Infections/*diagnostic imaging[MESH] |Databases, Factual[MESH] |Female[MESH] |Humans[MESH] |Male[MESH] |Middle Aged[MESH] |Pandemics[MESH] |Pneumonia, Viral/*diagnostic imaging[MESH] |ROC Curve[MESH] |Radiographic Image Interpretation, Computer-Assisted/*methods[MESH] |Radiography, Thoracic/*methods[MESH] |SARS-CoV-2[MESH]COVID-19 on Chest Radiographs: A Multireader Evaluation of an Artifici(epmc).pdf DeepDyve Pubget Overpricing