An overview of the National COVID-19 Chest Imaging Database: data quality and cohort analysis. Issue 11 (25th November 2021)

• Record Type: Journal Article
• Authors: Cushnan, Dominic; Bennett, Oscar; Berka, Rosalind; Bertolli, Ottavia; Chopra, Ashwin; Dorgham, Samie; Favaro, Alberto; Ganepola, Tara; Halling-Brown, Mark; Imreh, Gergely; Jacob, Joseph; Jefferson, Emily; Lemarchand, François; Schofield, Daniel; Wyatt, Jeremy C
• Is Part Of: GigaScience. Volume 10:Issue 11(2021)
• Publication Date: 2021-11-25
• Subjects: SARS-CoV2 -- COVID-19 -- thoracic imaging -- medical imaging -- machine learning; Information storage and retrieval systems -- Research -- Periodicals; Biology -- Research -- Periodicals; Medical sciences -- Research -- Periodicals; Database management -- Periodicals; 570.285
• DOI: 10.1093/gigascience/giab076
• Languages: English
• ISSNs: 2047-217X

Abstract

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.