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10.1007/978-3-030-71697-4_11 http://scihub22266oqcxt.onion/10.1007/978-3-030-71697-4_11 34279835!ä!34279835 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Adv+Exp+Med+Biol 2021 ; 1327 (ä): 139-147 Nephropedia Template TP {{tp|p=34279835|t=2021. Deep Learning Analysis in Prediction of COVID-19 Infection Status Using Chest CT Scan Features |pdf=|usr=}}{{34279835}} gab.com Text Deep Learning Analysis in Prediction of COVID-19 Infection Status Using Chest CT Scan Features JO: Adv Exp Med Biol http://www.kidney.de/pget.php?&tw=1&pmid=34279835 #FOAvip Background and aims Non-contrast chest computed tomography (CT) scanning is one of the important tools for evaluating of lung lesions. The aim of this study was to use a deep learning approach for predicting the outcome of patients with COVID-19 into two groups of critical and non-critical according to their CT features. Methods This was carried out as a retrospective study from March to April 2020 in Baqiyatallah Hospital, Tehran, Iran. From total of 1078 patients with COVID-19 pneumonia who underwent chest CT, 169 were critical cases and 909 were non-critical. Deep learning neural networks were used to classify samples into critical or non-critical ones according to the chest CT results. Results The best accuracy of prediction was seen by the presence of diffuse opacities and lesion distribution (both=0.91, 95% CI: 0.83-0.99). The largest sensitivity was achieved using lesion distribution (0.74, 95% CI: 0.55-0.93), and the largest specificity was for presence of diffuse opacities (0.95, 95% CI: 0.9-1). The total model showed an accuracy of 0.89 (95% CI: 0.79-0.99), and the corresponding sensitivity and specificity were 0.71 (95% CI: 0.51-0.91) and 0.93 (95% CI: 0.87-0.96), respectively. Conclusions The results showed that CT scan can accurately classify and predict critical and non-critical COVID-19 cases. Twit Text FOAVip Deep Learning Analysis in Prediction of COVID-19 Infection Status Using Chest CT Scan Features ????Adv Exp Med Biol http://www.kidney.de/pget.php?&tw=1&pmid=34279835 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34279835 #FOAvip English Wikipedia <ref>{{Cite pmid|34279835}}</ref> Deep Learning Analysis in Prediction of COVID-19 Infection Status Using Chest CT Scan Features #MMPMID34279835 Pourhoseingholi A; Vahedi M; Chaibakhsh S; Pourhoseingholi MA; Vahedian-Azimi A; Guest PC; Rahimi-Bashar F; Sahebkar A Adv Exp Med Biol 2021[]; 1327 (ä): 139-147 PMID34279835show ga Background and aims Non-contrast chest computed tomography (CT) scanning is one of the important tools for evaluating of lung lesions. The aim of this study was to use a deep learning approach for predicting the outcome of patients with COVID-19 into two groups of critical and non-critical according to their CT features. Methods This was carried out as a retrospective study from March to April 2020 in Baqiyatallah Hospital, Tehran, Iran. From total of 1078 patients with COVID-19 pneumonia who underwent chest CT, 169 were critical cases and 909 were non-critical. Deep learning neural networks were used to classify samples into critical or non-critical ones according to the chest CT results. Results The best accuracy of prediction was seen by the presence of diffuse opacities and lesion distribution (both=0.91, 95% CI: 0.83-0.99). The largest sensitivity was achieved using lesion distribution (0.74, 95% CI: 0.55-0.93), and the largest specificity was for presence of diffuse opacities (0.95, 95% CI: 0.9-1). The total model showed an accuracy of 0.89 (95% CI: 0.79-0.99), and the corresponding sensitivity and specificity were 0.71 (95% CI: 0.51-0.91) and 0.93 (95% CI: 0.87-0.96), respectively. Conclusions The results showed that CT scan can accurately classify and predict critical and non-critical COVID-19 cases. |*COVID-19[MESH] |*Deep Learning[MESH] |Humans[MESH] |Iran[MESH] |Lung[MESH] |Retrospective Studies[MESH] |SARS-CoV-2[MESH]Deep Learning Analysis in Prediction of COVID-19 Infection Status Usin(epmc).pdf DeepDyve Pubget Overpricing