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10.1148/radiol.2021203957 http://scihub22266oqcxt.onion/10.1148/radiol.2021203957 33399506!7993245!33399506     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       Radiology 2021 ; 299 (1): E204-E213 Nephropedia Template TP {{tp|p=33399506|t=2021. The RSNA International COVID-19 Open Radiology Database (RICORD) |pdf=|usr=}}{{33399506}} gab.com Text The RSNA International COVID-19 Open Radiology Database (RICORD) JO: Radiology http://www.kidney.de/pget.php?&tw=1&pmid=33399506 #FOAvip The coronavirus disease 2019 (COVID-19) pandemic is a global health care emergency. Although reverse-transcription polymerase chain reaction testing is the reference standard method to identify patients with COVID-19 infection, chest radiography and CT play a vital role in the detection and management of these patients. Prediction models for COVID-19 imaging are rapidly being developed to support medical decision making. However, inadequate availability of a diverse annotated data set has limited the performance and generalizability of existing models. To address this unmet need, the RSNA and Society of Thoracic Radiology collaborated to develop the RSNA International COVID-19 Open Radiology Database (RICORD). This database is the first multi-institutional, multinational, expert-annotated COVID-19 imaging data set. It is made freely available to the machine learning community as a research and educational resource for COVID-19 chest imaging. Pixel-level volumetric segmentation with clinical annotations was performed by thoracic radiology subspecialists for all COVID-19-positive thoracic CT scans. The labeling schema was coordinated with other international consensus panels and COVID-19 data annotation efforts, the European Society of Medical Imaging Informatics, the American College of Radiology, and the American Association of Physicists in Medicine. Study-level COVID-19 classification labels for chest radiographs were annotated by three radiologists, with majority vote adjudication by board-certified radiologists. RICORD consists of 240 thoracic CT scans and 1000 chest radiographs contributed from four international sites. It is anticipated that RICORD will ideally lead to prediction models that can demonstrate sustained performance across populations and health care systems. Twit Text FOAVip The RSNA International COVID-19 Open Radiology Database (RICORD) ????Radiology http://www.kidney.de/pget.php?&tw=1&pmid=33399506 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33399506 #FOAvip English Wikipedia <ref>{{Cite pmid|33399506}}</ref> The RSNA International COVID-19 Open Radiology Database (RICORD) #MMPMID33399506 Tsai EB; Simpson S; Lungren MP; Hershman M; Roshkovan L; Colak E; Erickson BJ; Shih G; Stein A; Kalpathy-Cramer J; Shen J; Hafez M; John S; Rajiah P; Pogatchnik BP; Mongan J; Altinmakas E; Ranschaert ER; Kitamura FC; Topff L; Moy L; Kanne JP; Wu CC Radiology 2021[Apr]; 299 (1): E204-E213 PMID33399506show ga The coronavirus disease 2019 (COVID-19) pandemic is a global health care emergency. Although reverse-transcription polymerase chain reaction testing is the reference standard method to identify patients with COVID-19 infection, chest radiography and CT play a vital role in the detection and management of these patients. Prediction models for COVID-19 imaging are rapidly being developed to support medical decision making. However, inadequate availability of a diverse annotated data set has limited the performance and generalizability of existing models. To address this unmet need, the RSNA and Society of Thoracic Radiology collaborated to develop the RSNA International COVID-19 Open Radiology Database (RICORD). This database is the first multi-institutional, multinational, expert-annotated COVID-19 imaging data set. It is made freely available to the machine learning community as a research and educational resource for COVID-19 chest imaging. Pixel-level volumetric segmentation with clinical annotations was performed by thoracic radiology subspecialists for all COVID-19-positive thoracic CT scans. The labeling schema was coordinated with other international consensus panels and COVID-19 data annotation efforts, the European Society of Medical Imaging Informatics, the American College of Radiology, and the American Association of Physicists in Medicine. Study-level COVID-19 classification labels for chest radiographs were annotated by three radiologists, with majority vote adjudication by board-certified radiologists. RICORD consists of 240 thoracic CT scans and 1000 chest radiographs contributed from four international sites. It is anticipated that RICORD will ideally lead to prediction models that can demonstrate sustained performance across populations and health care systems. |COVID-19/*diagnostic imaging[MESH] |Databases, Factual/*statistics & numerical data[MESH] |Global Health/*statistics & numerical data[MESH] |Humans[MESH] |Internationality[MESH] |Lung/*diagnostic imaging[MESH] |Radiography, Thoracic[MESH] |Radiology[MESH] |SARS-CoV-2[MESH] |Societies, Medical[MESH]The RSNA International COVID-19 Open Radiology Database (RICORD) (epmc).pdf DeepDyve Pubget Overpricing