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The coronavirus disease 2019 (COVID-19) pandemic is a global health care emergency. Although reverse-transcription polymerase chain reaction testing is the reference standard method to identify patients with COVID-19 infection, chest radiography and CT play a vital role in the detection and management of these patients. Prediction models for COVID-19 imaging are rapidly being developed to support medical decision making. However, inadequate availability of a diverse annotated data set has limited the performance and generalizability of existing models. To address this unmet need, the RSNA and Society of Thoracic Radiology collaborated to develop the RSNA International COVID-19 Open Radiology Database (RICORD). This database is the first multi-institutional, multinational, expert-annotated COVID-19 imaging data set. It is made freely available to the machine learning community as a research and educational resource for COVID-19 chest imaging. Pixel-level volumetric segmentation with clinical annotations was performed by thoracic radiology subspecialists for all COVID-19–positive thoracic CT scans. The labeling schema was coordinated with other international consensus panels and COVID-19 data annotation efforts, the European Society of Medical Imaging Informatics, the American College of Radiology, and the American Association of Physicists in Medicine. Study-level COVID-19 classification labels for chest radiographs were annotated by three radiologists, with majority vote adjudication by board-certified radiologists. RICORD consists of 240 thoracic CT scans and 1000 chest radiographs contributed from four international sites. It is anticipated that RICORD will ideally lead to prediction models that can demonstrate sustained performance across populations and health care systems. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Bai and Thomasian in this issue.

I ntracranial hemorrhage is a potentially life-threatening problem that has many direct and indirect causes. Accuracy in diagnosing the presence and type of intracranial hemorrhage is a critical part of effective treatment. Diagnosis is often an urgent procedure requiring review of medical images by highly trained specialists and sometimes necessitating confirmation through clinical history, vital signs, and laboratory examinations. The process is complicated and requires immediate identification for optimal treatment.Intracranial hemorrhage is a relatively common condition that has many causes, including trauma, stroke, aneurysm, vascular malformation, high blood pressure, illicit drugs, and blood clotting disorders (1). Neurologic consequences can vary extensively from headache to death depending upon the size, type, and location of the hemorrhage. The role of the radiologist is to detect the hemorrhage, characterize the type and cause of the hemorrhage, and to determine if the hemorrhage could be jeopardizing critical areas of the brain that might require immediate surgery.While all acute hemorrhages appear attenuated on CT images, the primary imaging features that help radiologists determine the cause of hemorrhage are the location, shape, and proximity to other structures. Intraparenchymal hemorrhage is blood that is located completely within the brain itself. Intraventricular or subarachnoid hemorrhage is blood that has leaked into the spaces of the brain that normally contain cerebrospinal fluid (the ventricles or subarachnoid cisterns, respectively). Extra-axial hemorrhage is blood that collects in the tissue coverings that surround the brain (eg, subdural or epidural subtypes). It is important to note that patients may exhibit more than one type of cerebral hemorrhage, which may appear on the same image or imaging study. Although small hemorrhages are typically less morbid than large hemorrhages, even a small hemorrhage can lead to death if it is in a critical location. Small hemorrhages also may herald future hemorrhages that could be fatal (eg, ruptured cerebral aneurysm). The presence or absence of hemorrhage may guide specific treatments (eg, stroke).Detection of cerebral hemorrhage with brain CT is a popular clinical use case for machine learning (2-5). Many of these early successful investigations were based upon relatively small datasets (hundreds of examinations) from single institutions. Chilamkurthy et al created a diverse brain CT dataset that was selected from 20 geographically distinct centers in India (more than 21 000 unique examinations). This was used to create smaller randomly selected subsets for validation and testing on common acute brain abnormalities (6). The ability for machine learning algorithms to generalize to "real-world" clinical imaging data from disparate institutions is paramount to successful use in the clinical environment.The intent for this challenge was to provide a large multiinstitutional and multinational dataset to help develop machine learning algorithms that ca...

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