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10.5152/dir.2019.20294 http://scihub22266oqcxt.onion/10.5152/dir.2019.20294 32436845!7490030!32436845     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32436845&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215       Diagn+Interv+Radiol 2020 ; 26 (5): 443-448 Nephropedia Template TP {{tp|p=32436845|t=2020. A review on the use of artificial intelligence for medical imaging of the lungs of patients with coronavirus disease 2019 |pdf=|usr=}}{{32436845}} gab.com Text A review on the use of artificial intelligence for medical imaging of the lungs of patients with coronavirus disease 2019 JO: Diagn Interv Radiol http://www.kidney.de/pget.php?&tw=1&pmid=32436845 #FOAvip The results of research on the use of artificial intelligence (AI) for medical imaging of the lungs of patients with coronavirus disease 2019 (COVID-19) has been published in various forms. In this study, we reviewed the AI for diagnostic imaging of COVID-19 pneumonia. PubMed, arXiv, medRxiv, and Google scholar were used to search for AI studies. There were 15 studies of COVID-19 that used AI for medical imaging. Of these, 11 studies used AI for computed tomography (CT) and 4 used AI for chest radiography. Eight studies presented independent test data, 5 used disclosed data, and 4 disclosed the AI source codes. The number of datasets ranged from 106 to 5941, with sensitivities ranging from 0.67-1.00 and specificities ranging from 0.81-1.00 for prediction of COVID-19 pneumonia. Four studies with independent test datasets showed a breakdown of the data ratio and reported prediction of COVID-19 pneumonia with sensitivity, specificity, and area under the curve (AUC). These 4 studies showed very high sensitivity, specificity, and AUC, in the range of 0.9-0.98, 0.91-0.96, and 0.96-0.99, respectively. Twit Text FOAVip A review on the use of artificial intelligence for medical imaging of the lungs of patients with coronavirus disease 2019 ????Diagn Interv Radiol http://www.kidney.de/pget.php?&tw=1&pmid=32436845 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32436845 #FOAvip English Wikipedia <ref>{{Cite pmid|32436845}}</ref> A review on the use of artificial intelligence for medical imaging of the lungs of patients with coronavirus disease 2019 #MMPMID32436845 Ito R; Iwano S; Naganawa S Diagn Interv Radiol 2020[Sep]; 26 (5): 443-448 PMID32436845show ga The results of research on the use of artificial intelligence (AI) for medical imaging of the lungs of patients with coronavirus disease 2019 (COVID-19) has been published in various forms. In this study, we reviewed the AI for diagnostic imaging of COVID-19 pneumonia. PubMed, arXiv, medRxiv, and Google scholar were used to search for AI studies. There were 15 studies of COVID-19 that used AI for medical imaging. Of these, 11 studies used AI for computed tomography (CT) and 4 used AI for chest radiography. Eight studies presented independent test data, 5 used disclosed data, and 4 disclosed the AI source codes. The number of datasets ranged from 106 to 5941, with sensitivities ranging from 0.67-1.00 and specificities ranging from 0.81-1.00 for prediction of COVID-19 pneumonia. Four studies with independent test datasets showed a breakdown of the data ratio and reported prediction of COVID-19 pneumonia with sensitivity, specificity, and area under the curve (AUC). These 4 studies showed very high sensitivity, specificity, and AUC, in the range of 0.9-0.98, 0.91-0.96, and 0.96-0.99, respectively. |*Artificial Intelligence[MESH] |*Betacoronavirus[MESH] |COVID-19[MESH] |Coronavirus Infections/*diagnostic imaging[MESH] |Humans[MESH] |Image Interpretation, Computer-Assisted/*methods[MESH] |Lung/*diagnostic imaging[MESH] |Pandemics[MESH] |Pneumonia, Viral/*diagnostic imaging[MESH] |Radiography/*methods[MESH] |Reproducibility of Results[MESH] |SARS-CoV-2[MESH] |Sensitivity and Specificity[MESH]A review on the use of artificial intelligence for medical imaging of (epmc).pdf DeepDyve Pubget Overpricing