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This retrospective NIfTI image dataset consists of unenhanced chest CTs from 652 632 patients with COVID-19 infections. The images were acquired at the point of care in an outbreak setting in China from patients with Reverse Transcription Polymerase Chain Reaction (RT-PCR) confirmation for the presence of SARS-CoV-2. Patients presented to a health care setting with a combination of symptoms, exposure to an infected patient, or travel history to an outbreak region. All patients had a positive RT-PCR for SARS-CoV-2 from a sample obtained within 1 day of the initial CT. CT exams were performed with intravenous contrast and a soft tissue reconstruction algorithm. The DICOM images were subsequently converted into NIfTI format. A multidisciplinary team trained several models using portions of this TCIA data set, along with additional CTs and manually annotated images from other sources. A classification model derived this work is described at: https://doi.org/10.1038/s41467-020-17971-2. The NVIDIA-related frameworks and models specific to this publication are available at no cost as part of the NVIDIA Clara Train SDK at https://ngc.nvidia.com/catalog/containers/nvidia:clara:ai-covid-19. This includes both inference-based pipelines for evaluation, as well as model weights for further training or fine tuning in outside institutions. In addition, a web-based version of this model (for research use only) with drag-and-drop functionality for evaluating individual scans can be found at https://marketplace.arterys.com/model/nvidiacovidCT. Uploading a CT yields a return email that contains results. |
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