TY - JOUR
T1 - The RSNA International COVID-19 Open Radiology Database (RICORD)
AU - Tsai, Emily B.
AU - Simpson, Scott
AU - Lungren, Matthew P.
AU - Hershman, Michelle
AU - Roshkovan, Leonid
AU - Colak, Errol
AU - Erickson, Bradley J.
AU - Shih, George
AU - Stein, Anouk
AU - Kalpathy-Cramer, Jayashree
AU - Shen, Jody
AU - Hafez, Mona
AU - John, Susan
AU - Rajiah, Prabhakar
AU - Pogatchnik, Brian P.
AU - Mongan, John
AU - Altinmakas, Emre
AU - Ranschaert, Erik R.
AU - Kitamura, Felipe C.
AU - Topff, Laurens
AU - Moy, Linda
AU - Kanne, Jeffrey P.
AU - Wu, Carol C.
N1 - Publisher Copyright:
© RSNA, 2021
PY - 2021/4
Y1 - 2021/4
N2 - 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.
AB - 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.
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U2 - 10.1148/RADIOL.2021203957
DO - 10.1148/RADIOL.2021203957
M3 - Article
C2 - 33399506
AN - SCOPUS:85103473822
SN - 0033-8419
VL - 299
SP - E204-E213
JO - Radiology
JF - Radiology
IS - 1
ER -