An overview of the National COVID-19 Chest Imaging Database: data quality and cohort analysis.
An overview of the National COVID-19 Chest Imaging Database: data quality and cohort analysis.
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DOI：
10.1093/gigascience/giab076
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发表时间：
2021-11-25
期刊：
影响因子：
9.2
通讯作者：
NCCID Collaborative
中科院分区：
文献类型：
--
作者：
Cushnan D;Bennett O;Berka R;Bertolli O;Chopra A;Dorgham S;Favaro A;Ganepola T;Halling-Brown M;Imreh G;Jacob J;Jefferson E;Lemarchand F;Schofield D;Wyatt JC;NCCID Collaborative
国家COVID-19胸部成像数据库（NCCID）是一个中央数据库，主要包含来自英国各地患者的胸部x射线和计算机断层扫描。该倡议的目标是支持更好地了解冠状病毒SARS-CoV-2疾病（COVID-19），并开发机器学习技术，以改善对COVID-19严重感染住院患者的护理。本文介绍了训练数据集，包括涵盖临床数据完整性的快照分析，以及用于各种用例（诊断，预后，纵向风险）的图像数据的可用性。另一项队列分析衡量了ncid在地理、人口和时间覆盖方面代表受covid -19影响的更广泛的英国人口的程度。NCCID提供通过各种成像设备获取的高质量DICOM图像；包括历史图像在内的多个时间点可用于患者子集。这种数量和多样性使该数据库非常适合开发与covid - 19相关的呼吸系统疾病的诊断/预后模型。历史图像和临床数据可能有助于长期风险分层，特别是当合并症数据的可用性通过与其他资源的联系而增加时。队列分析显示，在某些类别（如性别）中，英国COVID-19的总体统计数据与之保持良好一致，同时确定了数据收集方法有待改进的领域，特别是地理覆盖范围。NCCID是一个不断增长的资源，为研究人员提供了一个大型、高质量的数据库，既可以用来支持应对COVID-19大流行，也可以作为建立临床可行的医学成像模型的试验台。
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. 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. 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.
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影响因子：
19.7
作者：
Kanne JP;Bai H;Bernheim A;Chung M;Haramati LB;Kallmes DF;Little BP;Rubin GD;Sverzellati N
通讯作者：
Sverzellati N
影响因子：
4.6
作者：
Fernandes FT;de Oliveira TA;Teixeira CE;Batista AFM;Dalla Costa G;Chiavegatto Filho ADP
通讯作者：
Chiavegatto Filho ADP
DOI：
10.1016/s2213-2600(21)00005-9
发表时间：
2021-03
期刊：
The Lancet. Respiratory medicine
影响因子：
--
作者：
Kirby T
通讯作者：
Kirby T
影响因子：
4.6
作者：
Zargari Khuzani A;Heidari M;Shariati SA
通讯作者：
Shariati SA
影响因子：
24.3
作者：
Guan, Wei-jie;Liang, Wen-hua;He, Jian-xing
通讯作者：
He, Jian-xing