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10.7759/cureus.9448 http://scihub22266oqcxt.onion/10.7759/cureus.9448 32864270!7451075!32864270     free     free     free Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Cureus 2020 ; 12 (7): e9448 Nephropedia Template TP {{tp|p=32864270|t=2020. Predicting COVID-19 Pneumonia Severity on Chest X-ray With Deep Learning |pdf=|usr=}}{{32864270}} gab.com Text Predicting COVID-19 Pneumonia Severity on Chest X-ray With Deep Learning JO: Cureus http://www.kidney.de/pget.php?&tw=1&pmid=32864270 #FOAvip Introduction The need to streamline patient management for coronavirus disease-19 (COVID-19) has become more pressing than ever. Chest X-rays (CXRs) provide a non-invasive (potentially bedside) tool to monitor the progression of the disease. In this study, we present a severity score prediction model for COVID-19 pneumonia for frontal chest X-ray images. Such a tool can gauge the severity of COVID-19 lung infections (and pneumonia in general) that can be used for escalation or de-escalation of care as well as monitoring treatment efficacy, especially in the ICU. Methods Images from a public COVID-19 database were scored retrospectively by three blinded experts in terms of the extent of lung involvement as well as the degree of opacity. A neural network model that was pre-trained on large (non-COVID-19) chest X-ray datasets is used to construct features for COVID-19 images which are predictive for our task. Results This study finds that training a regression model on a subset of the outputs from this pre-trained chest X-ray model predicts our geographic extent score (range 0-8) with 1.14 mean absolute error (MAE) and our lung opacity score (range 0-6) with 0.78 MAE. Conclusions These results indicate that our model's ability to gauge the severity of COVID-19 lung infections could be used for escalation or de-escalation of care as well as monitoring treatment efficacy, especially in the ICU. To enable follow up work, we make our code, labels, and data available online. Twit Text FOAVip Predicting COVID-19 Pneumonia Severity on Chest X-ray With Deep Learning ????Cureus http://www.kidney.de/pget.php?&tw=1&pmid=32864270 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32864270 #FOAvip English Wikipedia <ref>{{Cite pmid|32864270}}</ref> Predicting COVID-19 Pneumonia Severity on Chest X-ray With Deep Learning #MMPMID32864270 Cohen JP; Dao L; Roth K; Morrison P; Bengio Y; Abbasi AF; Shen B; Mahsa HK; Ghassemi M; Li H; Duong TQ Cureus 2020[Jul]; 12 (7): e9448 PMID32864270show ga Introduction The need to streamline patient management for coronavirus disease-19 (COVID-19) has become more pressing than ever. Chest X-rays (CXRs) provide a non-invasive (potentially bedside) tool to monitor the progression of the disease. In this study, we present a severity score prediction model for COVID-19 pneumonia for frontal chest X-ray images. Such a tool can gauge the severity of COVID-19 lung infections (and pneumonia in general) that can be used for escalation or de-escalation of care as well as monitoring treatment efficacy, especially in the ICU. Methods Images from a public COVID-19 database were scored retrospectively by three blinded experts in terms of the extent of lung involvement as well as the degree of opacity. A neural network model that was pre-trained on large (non-COVID-19) chest X-ray datasets is used to construct features for COVID-19 images which are predictive for our task. Results This study finds that training a regression model on a subset of the outputs from this pre-trained chest X-ray model predicts our geographic extent score (range 0-8) with 1.14 mean absolute error (MAE) and our lung opacity score (range 0-6) with 0.78 MAE. Conclusions These results indicate that our model's ability to gauge the severity of COVID-19 lung infections could be used for escalation or de-escalation of care as well as monitoring treatment efficacy, especially in the ICU. To enable follow up work, we make our code, labels, and data available online. äPredicting COVID-19 Pneumonia Severity on Chest X-ray With Deep Learni(epmc).pdf DeepDyve Pubget Overpricing