We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress
Sign In
Advertise with Us

Download Mobile App


ATTENTION: Due to the COVID-19 PANDEMIC, many events are being rescheduled for a later date, converted into virtual venues, or altogether cancelled. Please check with the event organizer or website prior to planning for any forthcoming event.
18 May 2021 - 21 May 2021

RSNA Officially Publishes First Dataset of Annotated COVID-19 Images

By MedImaging International staff writers
Posted on 22 Dec 2020
Print article
The Radiological Society of North America (RSNA; Oak Brook, IL, USA) and the RSNA COVID-19 AI Task Force have 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).

Radiologists have played a pivotal role in managing the pandemic, particularly when other testing methods are unavailable or when clinicians seek imaging data to inform treatment decisions. 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. The RICORD data collection pathway enables radiology organizations to contribute data to RICORD safely and conveniently. It provides sites with guidance for data sharing and serves to standardize exam parameters, disease annotation terminology and clinical variables across these global efforts. Importantly, it connects to sustainable storage infrastructure via the US National Institutes of Health.

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. 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. The RSNA COVID-19 AI Task Force will continue to update and expand both the volume and variety of data available in RICORD. A collection of COVID-19 negative chest CT control cases is in the pipeline for publication soon, along with a labeled set of 1,000 COVID-19 positive chest X-rays. An even larger set of CT and X-ray images has been submitted to RICORD and is currently being processed.

“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.”

“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. “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.”

Related Links:
Radiological Society of North America

Print article


Industry News

view channel

Artificial Intelligence (AI) Program Accurately Predicts Lung Cancer Risk from CT Images

An artificial intelligence (AI) program accurately predicts the risk that lung nodules detected on screening CT will become cancerous, according to a study published in the journal Radiology.... Read more
Copyright © 2000-2021 Globetech Media. All rights reserved.