tetano
Editor, Senior Moderator
Gigascience
. 2021 Nov 25;10(11):giab076.
doi: 10.1093/gigascience/giab076.
An overview of the National COVID-19 Chest Imaging Database: data quality and cohort analysis
Dominic Cushnan[SUP] 1 [/SUP], Oscar Bennett[SUP] 2 [/SUP], Rosalind Berka[SUP] 2 [/SUP], Ottavia Bertolli[SUP] 2 [/SUP], Ashwin Chopra[SUP] 2 [/SUP], Samie Dorgham[SUP] 2 [/SUP], Alberto Favaro[SUP] 2 [/SUP], Tara Ganepola[SUP] 2 [/SUP], Mark Halling-Brown[SUP] 3 [/SUP], Gergely Imreh[SUP] 2 [/SUP], Joseph Jacob[SUP] 4 [/SUP], Emily Jefferson[SUP] 5 6 [/SUP], François Lemarchand[SUP] 1 [/SUP], Daniel Schofield[SUP] 1 [/SUP], Jeremy C Wyatt[SUP] 7 8 [/SUP], NCCID Collaborative
Affiliations
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.
Keywords: COVID-19; SARS-CoV2; machine learning; medical imaging; thoracic imaging.
. 2021 Nov 25;10(11):giab076.
doi: 10.1093/gigascience/giab076.
An overview of the National COVID-19 Chest Imaging Database: data quality and cohort analysis
Dominic Cushnan[SUP] 1 [/SUP], Oscar Bennett[SUP] 2 [/SUP], Rosalind Berka[SUP] 2 [/SUP], Ottavia Bertolli[SUP] 2 [/SUP], Ashwin Chopra[SUP] 2 [/SUP], Samie Dorgham[SUP] 2 [/SUP], Alberto Favaro[SUP] 2 [/SUP], Tara Ganepola[SUP] 2 [/SUP], Mark Halling-Brown[SUP] 3 [/SUP], Gergely Imreh[SUP] 2 [/SUP], Joseph Jacob[SUP] 4 [/SUP], Emily Jefferson[SUP] 5 6 [/SUP], François Lemarchand[SUP] 1 [/SUP], Daniel Schofield[SUP] 1 [/SUP], Jeremy C Wyatt[SUP] 7 8 [/SUP], NCCID Collaborative
Affiliations
- PMID: 34849869
- DOI: 10.1093/gigascience/giab076
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.
Keywords: COVID-19; SARS-CoV2; machine learning; medical imaging; thoracic imaging.