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10.1109/JBHI.2021.3069169 http://scihub22266oqcxt.onion/10.1109/JBHI.2021.3069169 33769939!8545163!33769939     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       IEEE+J+Biomed+Health+Inform 2021 ; 25 (6): 1892-1903 Nephropedia Template TP {{tp|p=33769939|t=2021. COVID-19 in CXR: From Detection and Severity Scoring to Patient Disease Monitoring |pdf=|usr=}}{{33769939}} gab.com Text COVID-19 in CXR: From Detection and Severity Scoring to Patient Disease Monitoring JO: IEEE J Biomed Health Inform http://www.kidney.de/pget.php?&tw=1&pmid=33769939 #FOAvip This work estimates the severity of pneumonia in COVID-19 patients and reports the findings of a longitudinal study of disease progression. It presents a deep learning model for simultaneous detection and localization of pneumonia in chest Xray (CXR) images, which is shown to generalize to COVID-19 pneumonia. The localization maps are utilized to calculate a "Pneumonia Ratio" which indicates disease severity. The assessment of disease severity serves to build a temporal disease extent profile for hospitalized patients. To validate the model's applicability to the patient monitoring task, we developed a validation strategy which involves a synthesis of Digital Reconstructed Radiographs (DRRs - synthetic Xray) from serial CT scans; we then compared the disease progression profiles that were generated from the DRRs to those that were generated from CT volumes. Twit Text FOAVip COVID-19 in CXR: From Detection and Severity Scoring to Patient Disease Monitoring ????IEEE J Biomed Health Inform http://www.kidney.de/pget.php?&tw=1&pmid=33769939 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33769939 #FOAvip English Wikipedia <ref>{{Cite pmid|33769939}}</ref> COVID-19 in CXR: From Detection and Severity Scoring to Patient Disease Monitoring #MMPMID33769939 Frid-Adar M; Amer R; Gozes O; Nassar J; Greenspan H IEEE J Biomed Health Inform 2021[Jun]; 25 (6): 1892-1903 PMID33769939show ga This work estimates the severity of pneumonia in COVID-19 patients and reports the findings of a longitudinal study of disease progression. It presents a deep learning model for simultaneous detection and localization of pneumonia in chest Xray (CXR) images, which is shown to generalize to COVID-19 pneumonia. The localization maps are utilized to calculate a "Pneumonia Ratio" which indicates disease severity. The assessment of disease severity serves to build a temporal disease extent profile for hospitalized patients. To validate the model's applicability to the patient monitoring task, we developed a validation strategy which involves a synthesis of Digital Reconstructed Radiographs (DRRs - synthetic Xray) from serial CT scans; we then compared the disease progression profiles that were generated from the DRRs to those that were generated from CT volumes. |*Radiography, Thoracic[MESH] |COVID-19/*diagnostic imaging/*pathology/virology[MESH] |Case-Control Studies[MESH] |Humans[MESH] |Monitoring, Physiologic/*methods[MESH] |Pneumonia, Viral/*diagnostic imaging/pathology/virology[MESH] |SARS-CoV-2/isolation & purification[MESH]COVID-19 in CXR: From Detection and Severity Scoring to Patient Diseas(epmc).pdf DeepDyve Pubget Overpricing