Background

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.

Purpose

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. The Radiological Society of North America (RSNA) assembled the RSNA International COVID-19 Open Radiology Database (RICORD) collection of COVID-related imaging datasets and expert annotations to support research and education. RICORD data will be incorporated in the Medical Imaging and Data Resource Center (MIDRC), a multi-institutional research data repository funded by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health.

Materials and Methods

This dataset was created through a collaboration between the RSNA and Society of Thoracic Radiology (STR). Clinical annotation by thoracic radiology subspecialists was performed for all COVID positive chest radiography (CXR) imaging studies using a labeling schema based upon guidelines for reporting classification of COVID-19 findings in CXRs (see Review of Chest Radiograph Findings of COVID-19 Pneumonia and Suggested Reporting Language, Journal of Thoracic Imaging).

Results

The RSNA International COVID-19 Open Annotated Radiology Database (RICORD) consists of 998 chest x-rays from 361 patients at four international sites annotated with diagnostic labels.

Patient Selection: Patients at least 18 years in age receiving positive diagnosis for COVID-19.

Data Abstract

1. 998 Chest x-ray examinations from 361 patients.

2. Annotations with labels:

   1. Classification

      • Typical Appearance
        Multifocal bilateral, peripheral opacities, and/or Opacities with rounded morphology
        Lower lung-predominant distribution (Required Feature - must be present with either or both of the first two opacity patterns)

      • Indeterminate Appearance
        Absence of typical findings AND Unilateral, central or upper lung predominant distribution of airspace disease

      • Atypical Appearance
        Pneumothorax or pleural effusion, Pulmonary Edema, Lobar Consolidation, Solitary lung nodule or mass, Diffuse tiny nodules, Cavity

      • Negative for Pneumonia
        No lung opacities

   2. Airspace Disease Grading
      Lungs are divided on frontal chest xray into 3 zones per lung (6 zones total). The upper zone extends from the apices to the superior hilum. The mid zone spans between the superior and inferior hilar margins. The lower zone extends from the inferior hilar margins to the costophrenic sulci.

      • Mild - Required if not negative for pneumonia
        Opacities in 1-2 lung zones

      • Moderate - Required if not negative for pneumonia
        Opacities in 3-4 lung zones

      • Severe - Required if not negative for pneumonia
        Opacities in >4 lung zones

3. Supporting clinical variables: MRN*, Age, Study Date*, Exam Description, Sex, Study UID*, Image Count, Modality, Testing Result, Specimen Source (* pseudonymous values).

Research Benefits

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 “ground truth” 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.

The MIDRC Data Commons is an AI‑ready, curated medical imaging dataset, currently encompassing over 135,000 public imaging studies (from a total collection of more than 300,000), sourced from chest X‑rays, chest CT scans, and later expanded to MRI, ultrasound, PET, and other anatomical regions across modalities. All images are stored in standard DICOM format, fully de‑identified, and paired with rich clinical metadata, including patient demographics, COVID‑19 status, imaging protocol tags, and harmonized descriptions based on LOINC standards. The dataset adheres to FAIR principles via the Gen3 Data Ecosystem, allowing registered users to build cohorts, query across metadata, and download images and annotations under a controlled data use agreement. t also features a sequestered (private) subset reserved specifically for AI validation/testing and regulatory benchmark purposes, separate from the open public dataset. The effort includes curation pipelines—covering de‑identification, abstraction, quality assessment, and ontology mapping—as well as semi‑automated annotation tools (e.g., DICOM SR/SEG, JSON) to support downstream AI development.

Background

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.

Purpose

The Radiological Society of North America (RSNA) assembled the RSNA International COVID-19 Open Radiology Database (RICORD), a collection of COVID-related imaging datasets and expert annotations to support research and education. The RICORD datasets are made freely available to the research community and will be incorporated in the Medical Imaging and Data Resource Center (MIDRC), a multi-institutional research data repository funded by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health.

Materials and Methods

MIDRC-RICORD dataset 1a was created through a collaboration between the RSNA and the Society of Thoracic Radiology (STR). Pixel-level volumetric segmentation with clinical annotations by thoracic radiology subspecialists was performed for all COVID positive thoracic computed tomography (CT) imaging studies in a labeling schema coordinated with other international consensus panels and COVID data annotation efforts.

Results

MIDRC-RICORD dataset 1a consists of 120 thoracic computed tomography (CT) scans from four international sites annotated with detailed segmentation and diagnostic labels.

Patient Selection: Patients at least 18 years in age receiving positive diagnosis for COVID-19.

Data Abstract

1. 120 Chest CT examinations (axial series only, any protocol).

2. Annotations comprised of

• a) Detailed segmentation of affected regions;
• b) Image-level labels (Infectious opacity, Infectious TIB/micronodules, Infectious cavity, Noninfectious nodule/mass, Atelectasis, Other noninfectious opacity)
• c) Exam-level diagnostic labels (Typical, Indeterminate, Atypical, Negative for pneumonia, Halo sign, Reversed halo sign, Reticular pattern w/o parenchymal opacity, Perilesional vessel enlargement, Bronchial wall thickening, Bronchiectasis, Subpleural curvilinear line, Effusion, Pleural thickening, Pneumothorax, Pericardial effusion, Lymphadenopathy, Pulmonary embolism, Normal lung, Infectious lung disease, Emphysema, Oncologic lung disease, Non-infectious inflammatory lung disease, Non-infectious interstitial, Fibrotic lung disease, Other lung disease)
• d) Exam-level procedure labels (With IV contrast, Without IV contrast, QA- inadequate motion/breathing, QA- inadequate insufficient inspiration, QA- inadequate low resolution, QA- inadequate incomplete lungs, QA- inadequate wrong body part/modality, Endotracheal tube, Central venous/arterial line, Nasogastric tube, Sternotomy wires, Pacemaker, Other support apparatus).

3. Supporting clinical variables: MRN*, Age, Study Date*, Exam Description, Sex, Study UID*, Image Count, Modality, Testing Result, Specimen Source (* pseudonymous values).

Research Benefits

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 “ground truth” 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.

The Medical Imaging & Data Resource Center (MIDRC) Data Commons supports the management, analysis and sharing of medical imaging data for the improvement of patient outcomes.

OpenM3Chest is a medical multimodal multitask dataset for diagnosing chest abnormalities with a focus on lung cancer screening. The raw data are from NLST and MIDRC, and the corresponding imaging data can be obtained from https://www.cancerimagingarchive.net/collection/nlst/ and https://www.midrc.org/