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10.1016/S2589-7500(21)00039-X http://scihub22266oqcxt.onion/10.1016/S2589-7500(21)00039-X 33773969!7990487!33773969     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Lancet+Digit+Health 2021 ; 3 (5): e286-e294 Nephropedia Template TP {{tp|p=33773969|t=2021. Prognostication of patients with COVID-19 using artificial intelligence based on chest x-rays and clinical data: a retrospective study |pdf=|usr=}}{{33773969}} gab.com Text Prognostication of patients with COVID-19 using artificial intelligence based on chest x-rays and clinical data: a retrospective study JO: Lancet Digit Health http://www.kidney.de/pget.php?&tw=1&pmid=33773969 #FOAvip BACKGROUND: Chest x-ray is a relatively accessible, inexpensive, fast imaging modality that might be valuable in the prognostication of patients with COVID-19. We aimed to develop and evaluate an artificial intelligence system using chest x-rays and clinical data to predict disease severity and progression in patients with COVID-19. METHODS: We did a retrospective study in multiple hospitals in the University of Pennsylvania Health System in Philadelphia, PA, USA, and Brown University affiliated hospitals in Providence, RI, USA. Patients who presented to a hospital in the University of Pennsylvania Health System via the emergency department, with a diagnosis of COVID-19 confirmed by RT-PCR and with an available chest x-ray from their initial presentation or admission, were retrospectively identified and randomly divided into training, validation, and test sets (7:1:2). Using the chest x-rays as input to an EfficientNet deep neural network and clinical data, models were trained to predict the binary outcome of disease severity (ie, critical or non-critical). The deep-learning features extracted from the model and clinical data were used to build time-to-event models to predict the risk of disease progression. The models were externally tested on patients who presented to an independent multicentre institution, Brown University affiliated hospitals, and compared with severity scores provided by radiologists. FINDINGS: 1834 patients who presented via the University of Pennsylvania Health System between March 9 and July 20, 2020, were identified and assigned to the model training (n=1285), validation (n=183), or testing (n=366) sets. 475 patients who presented via the Brown University affiliated hospitals between March 1 and July 18, 2020, were identified for external testing of the models. When chest x-rays were added to clinical data for severity prediction, area under the receiver operating characteristic curve (ROC-AUC) increased from 0.821 (95% CI 0.796-0.828) to 0.846 (0.815-0.852; p<0.0001) on internal testing and 0.731 (0.712-0.738) to 0.792 (0.780-0 .803; p<0.0001) on external testing. When deep-learning features were added to clinical data for progression prediction, the concordance index (C-index) increased from 0.769 (0.755-0.786) to 0.805 (0.800-0.820; p<0.0001) on internal testing and 0.707 (0.695-0.729) to 0.752 (0.739-0.764; p<0.0001) on external testing. The image and clinical data combined model had significantly better prognostic performance than combined severity scores and clinical data on internal testing (C-index 0.805 vs 0.781; p=0.0002) and external testing (C-index 0.752 vs 0.715; p<0.0001). INTERPRETATION: In patients with COVID-19, artificial intelligence based on chest x-rays had better prognostic performance than clinical data or radiologist-derived severity scores. Using artificial intelligence, chest x-rays can augment clinical data in predicting the risk of progression to critical illness in patients with COVID-19. FUNDING: Brown University, Amazon Web Services Diagnostic Development Initiative, Radiological Society of North America, National Cancer Institute and National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health. Twit Text FOAVip Prognostication of patients with COVID-19 using artificial intelligence based on chest x-rays and clinical data: a retrospective study ????Lancet Digit Health http://www.kidney.de/pget.php?&tw=1&pmid=33773969 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33773969 #FOAvip English Wikipedia <ref>{{Cite pmid|33773969}}</ref> Prognostication of patients with COVID-19 using artificial intelligence based on chest x-rays and clinical data: a retrospective study #MMPMID33773969 Jiao Z; Choi JW; Halsey K; Tran TML; Hsieh B; Wang D; Eweje F; Wang R; Chang K; Wu J; Collins SA; Yi TY; Delworth AT; Liu T; Healey TT; Lu S; Wang J; Feng X; Atalay MK; Yang L; Feldman M; Zhang PJL; Liao WH; Fan Y; Bai HX Lancet Digit Health 2021[May]; 3 (5): e286-e294 PMID33773969show ga BACKGROUND: Chest x-ray is a relatively accessible, inexpensive, fast imaging modality that might be valuable in the prognostication of patients with COVID-19. We aimed to develop and evaluate an artificial intelligence system using chest x-rays and clinical data to predict disease severity and progression in patients with COVID-19. METHODS: We did a retrospective study in multiple hospitals in the University of Pennsylvania Health System in Philadelphia, PA, USA, and Brown University affiliated hospitals in Providence, RI, USA. Patients who presented to a hospital in the University of Pennsylvania Health System via the emergency department, with a diagnosis of COVID-19 confirmed by RT-PCR and with an available chest x-ray from their initial presentation or admission, were retrospectively identified and randomly divided into training, validation, and test sets (7:1:2). Using the chest x-rays as input to an EfficientNet deep neural network and clinical data, models were trained to predict the binary outcome of disease severity (ie, critical or non-critical). The deep-learning features extracted from the model and clinical data were used to build time-to-event models to predict the risk of disease progression. The models were externally tested on patients who presented to an independent multicentre institution, Brown University affiliated hospitals, and compared with severity scores provided by radiologists. FINDINGS: 1834 patients who presented via the University of Pennsylvania Health System between March 9 and July 20, 2020, were identified and assigned to the model training (n=1285), validation (n=183), or testing (n=366) sets. 475 patients who presented via the Brown University affiliated hospitals between March 1 and July 18, 2020, were identified for external testing of the models. When chest x-rays were added to clinical data for severity prediction, area under the receiver operating characteristic curve (ROC-AUC) increased from 0.821 (95% CI 0.796-0.828) to 0.846 (0.815-0.852; p<0.0001) on internal testing and 0.731 (0.712-0.738) to 0.792 (0.780-0 .803; p<0.0001) on external testing. When deep-learning features were added to clinical data for progression prediction, the concordance index (C-index) increased from 0.769 (0.755-0.786) to 0.805 (0.800-0.820; p<0.0001) on internal testing and 0.707 (0.695-0.729) to 0.752 (0.739-0.764; p<0.0001) on external testing. The image and clinical data combined model had significantly better prognostic performance than combined severity scores and clinical data on internal testing (C-index 0.805 vs 0.781; p=0.0002) and external testing (C-index 0.752 vs 0.715; p<0.0001). INTERPRETATION: In patients with COVID-19, artificial intelligence based on chest x-rays had better prognostic performance than clinical data or radiologist-derived severity scores. Using artificial intelligence, chest x-rays can augment clinical data in predicting the risk of progression to critical illness in patients with COVID-19. FUNDING: Brown University, Amazon Web Services Diagnostic Development Initiative, Radiological Society of North America, National Cancer Institute and National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health. |*Artificial Intelligence[MESH] |*Prognosis[MESH] |*Radiography, Thoracic[MESH] |Adult[MESH] |Aged[MESH] |Aged, 80 and over[MESH] |COVID-19/*physiopathology[MESH] |Female[MESH] |Humans[MESH] |Male[MESH] |Middle Aged[MESH] |Retrospective Studies[MESH] |SARS-CoV-2[MESH] |Severity of Illness Index[MESH] |Tomography, X-Ray Computed[MESH] |United States[MESH]Prognostication of patients with COVID-19 using artificial intelligenc(epmc).pdf DeepDyve Pubget Overpricing