The Radiological Society of North America (RSNA) and the RSNA COVID-19 AI Task Force recently announced 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).
RSNA noted that 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.
The RSNA COVID-19 AI Task Force hopes that RICORD will serve as a definitive source for COVID-19 imaging data by combining the contributions and experiences of medical imaging specialists and radiology departments worldwide.
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.
“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, in a statement. “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. 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 annotation project, RSNA said.
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.
“RSNA is extremely proud to be part of the MIDRC effort,” said Curtis Langlotz, M.D., Ph.D., RSNA Board liaison for information technology and annual meeting, in a statement. “It will build a valuable repository of data for research to address the current pandemic and will serve as a model for how to collect and aggregate data to support imaging research.”
TCIA is a service provided by the National Cancer Institute (NCI) to the cancer imaging research community that de-identifies and hosts a large archive of medical images of cancer accessible for public download.
In recognition of the urgent need for access to COVID-19 related imaging data sets for research, NCI is dedicating a portion of TCIA resources to curate and host free and open to imaging data from patients with COVID-19. RSNA partnered with TCIA to leverage the infrastructure and processes it has in place to make these data publicly available. These data will also be incorporated into MIDRC when its data platform is fully in place in early 2021.
The MIDRC currently focuses on imaging and data of COVID-19, but in the future plans to expand to become a wider comprehensive resource for all Institutes at NIH, with focused medical imaging data commons of chronic disease (e.g., diabetes, chronic liver disease), coronary artery disease, COPD and emphysema, and other infectious pandemics. Additionally, the sequestering of some of the MIDRC data for validation and testing will provide a valuable resource for data science challenges and a path to long-term sustainability through industry support for translation to — and approval of — clinical use, which will impact public health worldwide.
