The RSNA International COVID-19 Open Annotated Radiology Database (RICORD).
Dramatic Case of Paradoxical Embolism.
Radiology. Cardiothoracic imaging
2020; 2 (4): e200360
Radiology-pathology Correlation in Recovered COVID-19, Demonstrating Organizing Pneumonia.
American journal of respiratory and critical care medicine
The coronavirus disease 2019 (COVID-19) pandemic is a global healthcare emergency. Although reverse transcriptase polymerase chain reaction (RT-PCR) is the reference standard method to identify patients with COVID-19 infection, chest radiographs and CT chest 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 dataset has limited the performance and generalizability of existing models. To address this unmet need, the RSNA and Society of Thoracic Radiology (STR) collaborated to develop the RSNA International COVID-19 Open Radiology Database (RICORD). This database is the first multi-institutional, multi-national expert annotated COVID-19 imaging dataset. 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 were performed by thoracic radiology subspecialists for all COVID positive thoracic CTs. The labeling schema was coordinated with other international consensus panels and COVID data annotation efforts, European Society of Medical Imaging Informatics (EUSOMII), the American College of Radiology (ACR) and the American Association of Physicists in Medicine (AAPM). Study level COVID 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 1,000 chest radiographs contributed from four international sites. We anticipate that the RICORD database will ideally lead to prediction models that can demonstrate sustained performance across populations and healthcare systems. See also the editorial by Bai and Thomasian.
View details for DOI 10.1148/radiol.2021203957
View details for PubMedID 33399506