1. Erratum to: An overview of the National COVID-19 Chest Imaging Database: data quality and cohort analysis
- Author
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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, and Oscar Bennett
- Subjects
medicine.medical_specialty ,Coronavirus disease 2019 (COVID-19) ,AcademicSubjects/SCI02254 ,medical imaging ,Health Informatics ,Data Note ,Cohort Studies ,thoracic imaging ,medicine ,Humans ,Medical physics ,Pandemics ,Chest imaging ,SARS-CoV-2 ,business.industry ,COVID-19 ,Data Accuracy ,Computer Science Applications ,machine learning ,Data quality ,SARS-CoV2 ,AcademicSubjects/SCI00960 ,Erratum ,Tomography, X-Ray Computed ,business ,Cohort study - 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.
- Published
- 2021
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