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10.1016/j.ejrad.2020.109402 http://scihub22266oqcxt.onion/10.1016/j.ejrad.2020.109402 33190102!7641539!33190102     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Eur+J+Radiol 2020 ; 133 (ä): 109402 Nephropedia Template TP {{tp|p=33190102|t=2020. Deep learning analysis provides accurate COVID-19 diagnosis on chest computed tomography |pdf=|usr=}}{{33190102}} gab.com Text Deep learning analysis provides accurate COVID-19 diagnosis on chest computed tomography JO: Eur J Radiol http://www.kidney.de/pget.php?&tw=1&pmid=33190102 #FOAvip INTRODUCTION: Computed Tomography is an essential diagnostic tool in the management of COVID-19. Considering the large amount of examinations in high case-load scenarios, an automated tool could facilitate and save critical time in the diagnosis and risk stratification of the disease. METHODS: A novel deep learning derived machine learning (ML) classifier was developed using a simplified programming approach and an open source dataset consisting of 6868 chest CT images from 418 patients which was split into training and validation subsets. The diagnostic performance was then evaluated and compared to experienced radiologists on an independent testing dataset. Diagnostic performance metrics were calculated using Receiver Operating Characteristics (ROC) analysis. Operating points with high positive (>10) and low negative (<0.01) likelihood ratios to stratify the risk of COVID-19 being present were identified and validated. RESULTS: The model achieved an overall accuracy of 0.956 (AUC) on an independent testing dataset of 90 patients. Both rule-in and rule out thresholds were identified and tested. At the rule-in operating point, sensitivity and specificity were 84.4 % and 93.3 % and did not differ from both radiologists (p?>?0.05). At the rule-out threshold, sensitivity (100 %) and specificity (60 %) differed significantly from the radiologists (p?<?0.05). Likelihood ratios and a Fagan nomogram provide prevalence independent test performance estimates. CONCLUSION: Accurate diagnosis of COVID-19 using a basic deep learning approach is feasible using open-source CT image data. In addition, the machine learning classifier provided validated rule-in and rule-out criteria could be used to stratify the risk of COVID-19 being present. Twit Text FOAVip Deep learning analysis provides accurate COVID-19 diagnosis on chest computed tomography ????Eur J Radiol http://www.kidney.de/pget.php?&tw=1&pmid=33190102 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33190102 #FOAvip English Wikipedia <ref>{{Cite pmid|33190102}}</ref> Deep learning analysis provides accurate COVID-19 diagnosis on chest computed tomography #MMPMID33190102 Javor D; Kaplan H; Kaplan A; Puchner SB; Krestan C; Baltzer P Eur J Radiol 2020[Dec]; 133 (ä): 109402 PMID33190102show ga INTRODUCTION: Computed Tomography is an essential diagnostic tool in the management of COVID-19. Considering the large amount of examinations in high case-load scenarios, an automated tool could facilitate and save critical time in the diagnosis and risk stratification of the disease. METHODS: A novel deep learning derived machine learning (ML) classifier was developed using a simplified programming approach and an open source dataset consisting of 6868 chest CT images from 418 patients which was split into training and validation subsets. The diagnostic performance was then evaluated and compared to experienced radiologists on an independent testing dataset. Diagnostic performance metrics were calculated using Receiver Operating Characteristics (ROC) analysis. Operating points with high positive (>10) and low negative (<0.01) likelihood ratios to stratify the risk of COVID-19 being present were identified and validated. RESULTS: The model achieved an overall accuracy of 0.956 (AUC) on an independent testing dataset of 90 patients. Both rule-in and rule out thresholds were identified and tested. At the rule-in operating point, sensitivity and specificity were 84.4 % and 93.3 % and did not differ from both radiologists (p?>?0.05). At the rule-out threshold, sensitivity (100 %) and specificity (60 %) differed significantly from the radiologists (p? |*Deep Learning[MESH] |COVID-19/*diagnostic imaging[MESH] |Female[MESH] |Humans[MESH] |Lung/*diagnostic imaging[MESH] |Male[MESH] |Middle Aged[MESH] |ROC Curve[MESH] |Radiographic Image Interpretation, Computer-Assisted/*methods[MESH] |Reproducibility of Results[MESH] |Sensitivity and Specificity[MESH]Deep learning analysis provides accurate COVID-19 diagnosis on chest c(epmc).pdf DeepDyve Pubget Overpricing