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Erratum to: An overview of the National COVID-19 Chest Imaging Database: data quality and cohort analysis

Authors :
Mark D. Halling-Brown
Daniel Schofield
Rosalind Berka
Ottavia Bertolli
Joseph Jacob
Ashwin Chopra
Dominic Cushnan
Emily Jefferson
François Lemarchand
Jeremy C Wyatt
Alberto Favaro
Nccid Collaborative
Tara Ganepola
Gergely Imreh
Samie Dorgham
Oscar Bennett
Source :
GigaScience
Publication Year :
2021
Publisher :
Oxford University Press (OUP), 2021.

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.

Details

ISSN :
2047217X
Volume :
10
Database :
OpenAIRE
Journal :
GigaScience
Accession number :
edsair.doi.dedup.....36ed7c3b801ed873398326888d6b3790
Full Text :
https://doi.org/10.1093/gigascience/giab083