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10.3390/diagnostics11010041 http://scihub22266oqcxt.onion/10.3390/diagnostics11010041 33396587!7823620!33396587     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       Diagnostics+(Basel) 2020 ; 11 (1): ä Nephropedia Template TP {{tp|p=33396587|t=2020. Development and Validation of an Automated Radiomic CT Signature for Detecting COVID-19 |pdf=|usr=}}{{33396587}} gab.com Text Development and Validation of an Automated Radiomic CT Signature for Detecting COVID-19 JO: Diagnostics (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=33396587 #FOAvip The coronavirus disease 2019 (COVID-19) outbreak has reached pandemic status. Drastic measures of social distancing are enforced in society and healthcare systems are being pushed to and beyond their limits. To help in the fight against this threat on human health, a fully automated AI framework was developed to extract radiomics features from volumetric chest computed tomography (CT) exams. The detection model was developed on a dataset of 1381 patients (181 COVID-19 patients plus 1200 non COVID control patients). A second, independent dataset of 197 RT-PCR confirmed COVID-19 patients and 500 control patients was used to assess the performance of the model. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC). The model had an AUC of 0.882 (95% CI: 0.851-0.913) in the independent test dataset (641 patients). The optimal decision threshold, considering the cost of false negatives twice as high as the cost of false positives, resulted in an accuracy of 85.18%, a sensitivity of 69.52%, a specificity of 91.63%, a negative predictive value (NPV) of 94.46% and a positive predictive value (PPV) of 59.44%. Benchmarked against RT-PCR confirmed cases of COVID-19, our AI framework can accurately differentiate COVID-19 from routine clinical conditions in a fully automated fashion. Thus, providing rapid accurate diagnosis in patients suspected of COVID-19 infection, facilitating the timely implementation of isolation procedures and early intervention. Twit Text FOAVip Development and Validation of an Automated Radiomic CT Signature for Detecting COVID-19 ????Diagnostics (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=33396587 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33396587 #FOAvip English Wikipedia <ref>{{Cite pmid|33396587}}</ref> Development and Validation of an Automated Radiomic CT Signature for Detecting COVID-19 #MMPMID33396587 Guiot J; Vaidyanathan A; Deprez L; Zerka F; Danthine D; Frix AN; Thys M; Henket M; Canivet G; Mathieu S; Eftaxia E; Lambin P; Tsoutzidis N; Miraglio B; Walsh S; Moutschen M; Louis R; Meunier P; Vos W; Leijenaar RTH; Lovinfosse P Diagnostics (Basel) 2020[Dec]; 11 (1): ä PMID33396587show ga The coronavirus disease 2019 (COVID-19) outbreak has reached pandemic status. Drastic measures of social distancing are enforced in society and healthcare systems are being pushed to and beyond their limits. To help in the fight against this threat on human health, a fully automated AI framework was developed to extract radiomics features from volumetric chest computed tomography (CT) exams. The detection model was developed on a dataset of 1381 patients (181 COVID-19 patients plus 1200 non COVID control patients). A second, independent dataset of 197 RT-PCR confirmed COVID-19 patients and 500 control patients was used to assess the performance of the model. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC). The model had an AUC of 0.882 (95% CI: 0.851-0.913) in the independent test dataset (641 patients). The optimal decision threshold, considering the cost of false negatives twice as high as the cost of false positives, resulted in an accuracy of 85.18%, a sensitivity of 69.52%, a specificity of 91.63%, a negative predictive value (NPV) of 94.46% and a positive predictive value (PPV) of 59.44%. Benchmarked against RT-PCR confirmed cases of COVID-19, our AI framework can accurately differentiate COVID-19 from routine clinical conditions in a fully automated fashion. Thus, providing rapid accurate diagnosis in patients suspected of COVID-19 infection, facilitating the timely implementation of isolation procedures and early intervention. äDevelopment and Validation of an Automated Radiomic CT Signature for D(epmc).pdf DeepDyve Pubget Overpricing