Using imaging to combat a pandemic: rationale for developing the UK National COVID-19 Chest Imaging Database

The scale of the COVID-19 pandemic has resulted in the acquisition of huge volumes of imaging data.
Traditionally, research using imaging data constituted collation of data within single hospitals or groups
of hospitals at most. Endeavours on a local scale have the constraint that not all patient subgroups or
disease manifestations might be captured in the collected data. It has long been recognised that there
is an acute need to curate larger, more comprehensive datasets to better understand a disease.
COVID-19 has arrived in an era where advances in computational power, aligned with an increased
availability of big data and the development of self-learning neural networks, have begun to redefine
research in medicine. In recent years, computer algorithms trained on imaging data, widely available
on the internet, have been adapted to the task of medical image analysis [5, 6]. For computer
algorithms to be successfully applied to medical image analysis, it is imperative that they train on large
volumes and representative examples of imaging data. These are typically orders of magnitude larger
than traditional imaging research datasets, and beyond the capacity of traditional research
e-infrastructure

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