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10.1007/s00330-021-07797-x http://scihub22266oqcxt.onion/10.1007/s00330-021-07797-x 33738595!7971359!33738595     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       Eur+Radiol 2021 ; 31 (9): 7192-7201 Nephropedia Template TP {{tp|p=33738595|t=2021. AI detection of mild COVID-19 pneumonia from chest CT scans |pdf=|usr=}}{{33738595}} gab.com Text AI detection of mild COVID-19 pneumonia from chest CT scans JO: Eur Radiol http://www.kidney.de/pget.php?&tw=1&pmid=33738595 #FOAvip OBJECTIVES: An artificial intelligence model was adopted to identify mild COVID-19 pneumonia from computed tomography (CT) volumes, and its diagnostic performance was then evaluated. METHODS: In this retrospective multicenter study, an atrous convolution-based deep learning model was established for the computer-assisted diagnosis of mild COVID-19 pneumonia. The dataset included 2087 chest CT exams collected from four hospitals between 1 January 2019 and 31 May 2020. The true positive rate, true negative rate, receiver operating characteristic curve, area under the curve (AUC) and convolutional feature map were used to evaluate the model. RESULTS: The proposed deep learning model was trained on 1538 patients and tested on an independent testing cohort of 549 patients. The overall sensitivity was 91.5% (195/213; p < 0.001, 95% CI: 89.2-93.9%), the overall specificity was 90.5% (304/336; p < 0.001, 95% CI: 88.0-92.9%) and the general AUC value was 0.955 (p < 0.001). CONCLUSIONS: A deep learning model can accurately detect COVID-19 and serve as an important supplement to the COVID-19 reverse transcription-polymerase chain reaction (RT-PCR) test. KEY POINTS: * The implementation of a deep learning model to identify mild COVID-19 pneumonia was confirmed to be effective and feasible. * The strategy of using a binary code instead of the region of interest label to identify mild COVID-19 pneumonia was verified. * This AI model can assist in the early screening of COVID-19 without interfering with normal clinical examinations. Twit Text FOAVip AI detection of mild COVID-19 pneumonia from chest CT scans ????Eur Radiol http://www.kidney.de/pget.php?&tw=1&pmid=33738595 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33738595 #FOAvip English Wikipedia <ref>{{Cite pmid|33738595}}</ref> AI detection of mild COVID-19 pneumonia from chest CT scans #MMPMID33738595 Yao JC; Wang T; Hou GH; Ou D; Li W; Zhu QD; Chen WC; Yang C; Wang LJ; Wang LP; Fan LY; Shi KY; Zhang J; Xu D; Li YQ Eur Radiol 2021[Sep]; 31 (9): 7192-7201 PMID33738595show ga OBJECTIVES: An artificial intelligence model was adopted to identify mild COVID-19 pneumonia from computed tomography (CT) volumes, and its diagnostic performance was then evaluated. METHODS: In this retrospective multicenter study, an atrous convolution-based deep learning model was established for the computer-assisted diagnosis of mild COVID-19 pneumonia. The dataset included 2087 chest CT exams collected from four hospitals between 1 January 2019 and 31 May 2020. The true positive rate, true negative rate, receiver operating characteristic curve, area under the curve (AUC) and convolutional feature map were used to evaluate the model. RESULTS: The proposed deep learning model was trained on 1538 patients and tested on an independent testing cohort of 549 patients. The overall sensitivity was 91.5% (195/213; p < 0.001, 95% CI: 89.2-93.9%), the overall specificity was 90.5% (304/336; p < 0.001, 95% CI: 88.0-92.9%) and the general AUC value was 0.955 (p < 0.001). CONCLUSIONS: A deep learning model can accurately detect COVID-19 and serve as an important supplement to the COVID-19 reverse transcription-polymerase chain reaction (RT-PCR) test. KEY POINTS: * The implementation of a deep learning model to identify mild COVID-19 pneumonia was confirmed to be effective and feasible. * The strategy of using a binary code instead of the region of interest label to identify mild COVID-19 pneumonia was verified. * This AI model can assist in the early screening of COVID-19 without interfering with normal clinical examinations. |*COVID-19[MESH] |*Deep Learning[MESH] |Artificial Intelligence[MESH] |Humans[MESH] |Retrospective Studies[MESH] |SARS-CoV-2[MESH]AI detection of mild COVID-19 pneumonia from chest CT scans (epmc).pdf DeepDyve Pubget Overpricing