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10.1007/s00259-020-04953-1 http://scihub22266oqcxt.onion/10.1007/s00259-020-04953-1 32666395!7358997!32666395     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32666395&cmd=llinks): Failed to open stream: HTTP request failed! 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Automated detection and quantification of COVID-19 pneumonia: CT imaging analysis by a deep learning-based software |pdf=|usr=}}{{32666395}} gab.com Text Automated detection and quantification of COVID-19 pneumonia: CT imaging analysis by a deep learning-based software JO: Eur J Nucl Med Mol Imaging http://www.kidney.de/pget.php?&tw=1&pmid=32666395 #FOAvip BACKGROUND: The novel coronavirus disease 2019 (COVID-19) is an emerging worldwide threat to public health. While chest computed tomography (CT) plays an indispensable role in its diagnosis, the quantification and localization of lesions cannot be accurately assessed manually. We employed deep learning-based software to aid in detection, localization and quantification of COVID-19 pneumonia. METHODS: A total of 2460 RT-PCR tested SARS-CoV-2-positive patients (1250 men and 1210 women; mean age, 57.7 +/- 14.0 years (age range, 11-93 years) were retrospectively identified from Huoshenshan Hospital in Wuhan from February 11 to March 16, 2020. Basic clinical characteristics were reviewed. The uAI Intelligent Assistant Analysis System was used to assess the CT scans. RESULTS: CT scans of 2215 patients (90%) showed multiple lesions of which 36 (1%) and 50 patients (2%) had left and right lung infections, respectively (> 50% of each affected lung's volume), while 27 (1%) had total lung infection (> 50% of the total volume of both lungs). Overall, 298 (12%), 778 (32%) and 1300 (53%) patients exhibited pure ground glass opacities (GGOs), GGOs with sub-solid lesions and GGOs with both sub-solid and solid lesions, respectively. Moreover, 2305 (94%) and 71 (3%) patients presented primarily with GGOs and sub-solid lesions, respectively. Elderly patients (>/= 60 years) were more likely to exhibit sub-solid lesions. The generalized linear mixed model showed that the dorsal segment of the right lower lobe was the favoured site of COVID-19 pneumonia. CONCLUSION: Chest CT combined with analysis by the uAI Intelligent Assistant Analysis System can accurately evaluate pneumonia in COVID-19 patients. Twit Text FOAVip Automated detection and quantification of COVID-19 pneumonia: CT imaging analysis by a deep learning-based software ????Eur J Nucl Med Mol Imaging http://www.kidney.de/pget.php?&tw=1&pmid=32666395 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32666395 #FOAvip English Wikipedia <ref>{{Cite pmid|32666395}}</ref> Automated detection and quantification of COVID-19 pneumonia: CT imaging analysis by a deep learning-based software #MMPMID32666395 Zhang HT; Zhang JS; Zhang HH; Nan YD; Zhao Y; Fu EQ; Xie YH; Liu W; Li WP; Zhang HJ; Jiang H; Li CM; Li YY; Ma RN; Dang SK; Gao BB; Zhang XJ; Zhang T Eur J Nucl Med Mol Imaging 2020[Oct]; 47 (11): 2525-2532 PMID32666395show ga BACKGROUND: The novel coronavirus disease 2019 (COVID-19) is an emerging worldwide threat to public health. While chest computed tomography (CT) plays an indispensable role in its diagnosis, the quantification and localization of lesions cannot be accurately assessed manually. We employed deep learning-based software to aid in detection, localization and quantification of COVID-19 pneumonia. METHODS: A total of 2460 RT-PCR tested SARS-CoV-2-positive patients (1250 men and 1210 women; mean age, 57.7 +/- 14.0 years (age range, 11-93 years) were retrospectively identified from Huoshenshan Hospital in Wuhan from February 11 to March 16, 2020. Basic clinical characteristics were reviewed. The uAI Intelligent Assistant Analysis System was used to assess the CT scans. RESULTS: CT scans of 2215 patients (90%) showed multiple lesions of which 36 (1%) and 50 patients (2%) had left and right lung infections, respectively (> 50% of each affected lung's volume), while 27 (1%) had total lung infection (> 50% of the total volume of both lungs). Overall, 298 (12%), 778 (32%) and 1300 (53%) patients exhibited pure ground glass opacities (GGOs), GGOs with sub-solid lesions and GGOs with both sub-solid and solid lesions, respectively. Moreover, 2305 (94%) and 71 (3%) patients presented primarily with GGOs and sub-solid lesions, respectively. Elderly patients (>/= 60 years) were more likely to exhibit sub-solid lesions. The generalized linear mixed model showed that the dorsal segment of the right lower lobe was the favoured site of COVID-19 pneumonia. CONCLUSION: Chest CT combined with analysis by the uAI Intelligent Assistant Analysis System can accurately evaluate pneumonia in COVID-19 patients. |*Betacoronavirus/isolation & purification[MESH] |*Deep Learning[MESH] |*Pandemics[MESH] |Adolescent[MESH] |Adult[MESH] |Aged[MESH] |Aged, 80 and over[MESH] |COVID-19[MESH] |COVID-19 Testing[MESH] |Child[MESH] |Clinical Laboratory Techniques[MESH] |Coronavirus Infections/diagnosis/*diagnostic imaging[MESH] |Female[MESH] |Humans[MESH] |Linear Models[MESH] |Lung/*diagnostic imaging[MESH] |Male[MESH] |Middle Aged[MESH] |Multidetector Computed Tomography/*methods[MESH] |Pneumonia, Viral/*diagnostic imaging[MESH] |Retrospective Studies[MESH] |Reverse Transcriptase Polymerase Chain Reaction[MESH] |SARS-CoV-2[MESH] |Software[MESH]Automated detection and quantification of COVID-19 pneumonia: CT imagi(epmc).pdf DeepDyve Pubget Overpricing