{"id":5626,"date":"2023-09-04T03:13:35","date_gmt":"2023-09-04T03:13:35","guid":{"rendered":"https:\/\/cm.vastapps.dev\/tcia-collection\/midrc-ricord-1b\/"},"modified":"2023-09-13T12:00:25","modified_gmt":"2023-09-13T12:00:25","slug":"midrc-ricord-1b","status":"publish","type":"tcia_collection","link":"https:\/\/cm.vastapps.dev\/tcia-collection\/midrc-ricord-1b\/","title":{"rendered":"MIDRC-RICORD-1B"},"featured_media":0,"template":"","citation-tax":[],"cancer_types":["COVID-19 (non-cancer)"],"citations":[4527,4528,2925],"collection_doi":"10.7937\/31V8-4A40","collection_download_info":"Click the Versions tab for more info about data releases.\nPlease contact help@cancerimagingarchive.net<\/a>\u00a0 with any questions regarding usage.","collection_downloads":[5119,5120],"full_export":"

Summary<\/h1>

Background<\/span><\/u><\/p>

The COVID-19 pandemic is a global healthcare emergency. Prediction models for COVID-19 imaging are rapidly being developed to support medical decision making in imaging. However, inadequate availability of a diverse annotated dataset has limited the performance and generalizability of existing models.<\/span><\/p>

Purpose<\/span><\/u><\/p>

To create the first multi-institutional, multi-national expert annotated COVID-19 imaging dataset made freely available to the machine learning community as a research and educational resource for COVID-19 chest imaging.\u00a0The Radiological Society of North America (RSNA) assembled the\u00a0RSNA International COVID-19 Open Radiology Database (RICORD)<\/a>\u00a0collection\u00a0of COVID-related imaging datasets and expert annotations to support research and education.\u00a0RICORD\u00a0data will be incorporated in the\u00a0Medical Imaging\u00a0and\u00a0Data Resource Center (MIDRC)<\/a>,\u00a0a\u00a0multi-institutional research data repository funded by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health.<\/span><\/span><\/p>

Materials and Methods<\/span><\/u><\/p>

This dataset was a collaboration between the RSNA and Society of Thoracic Radiology (STR). <\/span><\/p>

Results<\/span><\/u><\/p>

The RSNA International COVID-19 Open Annotated Radiology Database (RICORD) release 1b consists of 120 thoracic computed tomography (CT) scans of COVID negative patients from four international sites.<\/span><\/p>

Patient Selection: Patients at least 18 years in age receiving negative diagnosis for COVID-19.<\/span><\/p>

Data Abstract<\/span><\/u><\/p>

  1. 120 de-identified Thoracic CT scans from COVID negative patients.<\/span><\/p><\/li>

  2. Supporting clinical variables: MRN*, Age, Exam Date\/Time*, Exam Description, Sex, Study UID*, Image Count, Modality, Symptomatic, Testing Result, Specimen Source (* pseudonymous values).<\/span><\/p><\/li><\/ol>

    Research Benefits<\/span><\/u><\/p>

    As this is a public dataset, RICORD is available for non-commercial use (and further enrichment) by the research and education communities which may include development of educational resources for COVID-19, use of RICORD to create AI systems for diagnosis and quantification, benchmarking performance for existing solutions, exploration of distributed\/federated learning, further annotation or data augmentation efforts, and evaluation of the examinations for disease entities beyond COVID-19 pneumonia. Deliberate consideration of the detailed annotation schema, demographics, and other included meta-data will be critical when generating cohorts with RICORD, particularly as more public COVID-19 imaging datasets are made available via complementary and parallel efforts. It is important to emphasize that there are limitations to the clinical \u201cground truth\u201d as the SARS-CoV-2 RT-PCR tests have widely documented limitations and are subject to both false-negative and false-positive results which impact the distribution of the included imaging data, and may have led to an unknown epidemiologic distortion of patients based on the inclusion criteria. These limitations notwithstanding, RICORD has achieved the stated objectives for data complexity, heterogeneity, and high-quality expert annotations as a comprehensive COVID-19 thoracic imaging data resource.<\/span><\/p>

    Acknowledgements<\/span><\/h3>

    We would like to acknowledge the individuals and institutions that have provided data for this collection:\u00a0\u00a0Data in RICORD will be made available through the Medical Imagining Data Resource Center, funded through a contract with the National Institute for Biomedical Imaging and Bioengineering (NIBIB).<\/span><\/p>

    <\/p>