Cushnan, D;
Bennett, O;
Berka, R;
Bertolli, O;
Chopra, A;
Dorgham, S;
Favaro, A;
... NCCID Collaborative, .; + view all
(2021)
An overview of the National COVID-19 Chest Imaging Database: data quality and cohort analysis.
Gigascience
, 10
(11)
, Article giab076. 10.1093/gigascience/giab076.
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Abstract
Background: The National COVID-19 Chest Imaging Database (NCCID) is a centralized database containing mainly chest X-rays and computed tomography scans from patients across the UK. The objective of the initiative is to support a better understanding of the coronavirus SARS-CoV-2 disease (COVID-19) and the development of machine learning technologies that will improve care for patients hospitalized with a severe COVID-19 infection. This article introduces the training dataset, including a snapshot analysis covering the completeness of clinical data, and availability of image data for the various use-cases (diagnosis, prognosis, longitudinal risk). An additional cohort analysis measures how well the NCCID represents the wider COVID-19–affected UK population in terms of geographic, demographic, and temporal coverage. // Findings: The NCCID offers high-quality DICOM images acquired across a variety of imaging machinery; multiple time points including historical images are available for a subset of patients. This volume and variety make the database well suited to development of diagnostic/prognostic models for COVID-associated respiratory conditions. Historical images and clinical data may aid long-term risk stratification, particularly as availability of comorbidity data increases through linkage to other resources. The cohort analysis revealed good alignment to general UK COVID-19 statistics for some categories, e.g., sex, whilst identifying areas for improvements to data collection methods, particularly geographic coverage. // Conclusion: The NCCID is a growing resource that provides researchers with a large, high-quality database that can be leveraged both to support the response to the COVID-19 pandemic and as a test bed for building clinically viable medical imaging models.
| Type: | Article |
|---|---|
| Title: | An overview of the National COVID-19 Chest Imaging Database: data quality and cohort analysis |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/gigascience/giab076 |
| Publisher version: | https://doi.org/10.1093/gigascience/giab076 |
| Language: | English |
| Additional information: | © The Author(s) 2021. Published by Oxford University Press GigaScience. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | COVID-19, SARS-CoV2, machine learning, medical imaging, thoracic imaging |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine > Respiratory Medicine |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10140373 |
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