OAK BROOK, Ill. (Dec. 18, 2020) — The Radiological Society of North America (RSNA) and the RSNA COVID-19 AI Task Force announced today that the first annotated data set from the RSNA International COVID-19 Open Radiology Database (RICORD) has been published by The Cancer Imaging Archive (TCIA).
The COVID-19 pandemic is a global public health crisis. Although prediction models for COVID-19 imaging have been developed to support medical decision making, the lack of a diverse annotated data set has hindered the capabilities of these models. RSNA launched RICORD in mid-2020 with the goal of building the largest open database of anonymized COVID-19 medical images in the world. It is being made freely available to the global research and education communities to gain new insights, apply new tools such as artificial intelligence and deep learning, and accelerate clinical recognition of this novel disease.
Created through a collaboration between RSNA and the Society of Thoracic Radiology, the initial group consists of 120 COVID-19 positive chest CT images from four international sites.
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“RSNA was able to draw on relationships established from prior machine learning challenges to quickly put together a COVID-19 AI Task Force,” said Carol Wu, M.D., a radiologist at MD Anderson Cancer Center and a member of the RSNA task force. “Contributing sites, already proficient at sharing data with RSNA, were able to quickly process necessary legal agreements, identify suitable cases, perform image de-identification and transfer the images in record speed.”
The image data was then annotated with detailed segmentation and classification labels. The label schema is based on guidelines outlined in the “Radiological Society of North America Expert Consensus Document on Reporting Chest CT Findings Related to COVID-19: Endorsed by the Society of Thoracic Radiology, the American College of Radiology, and RSNA” published in Radiology. Two teams of radiologists led by Scott Simpson, D.O., of the University of Pennsylvania, and Emily Tsai, M.D., of Stanford University, completed the painstaking and invaluable annotation project.
This data set represents the first published component of RICORD, and RSNA’s first contribution to the Medical Imaging and Data Resource Center (MIDRC), a consortium for rapid and flexible collection, artificial intelligence analysis and dissemination of imaging and associated data. Jointly developed by RSNA, the American College of Radiology and the American Association of Physicists in Medicine, MIDRC is funded by the National Institute of Biomedical Imaging and Bioengineering and hosted by the University of Chicago.