An extensible big data software architecture managing a research resource of real-world clinical radiology data linked to other health data from the whole Scottish population. Issue 10 (29th September 2020)

• Record Type: Journal Article
• Authors: Nind, Thomas; Sutherland, James; McAllister, Gordon; Hardy, Douglas; Hume, Ally; MacLeod, Ruairidh; Caldwell, Jacqueline; Krueger, Susan; Tramma, Leandro; Teviotdale, Ross; Abdelatif, Mohammed; Gillen, Kenny; Ward, Joe; Scobbie, Donald; Baillie, Ian; Brooks, Andrew; Prodan, Bianca; Kerr, William; Sloan-Murphy, Dominic; Herrera, Juan F R; McManus, Dan; Morris, Carole; Sinclair, Carol; Baxter, Rob; Parsons, Mark; Morris, Andrew; Jefferson, Emily
• Is Part Of: GigaScience. Volume 9:Issue 10(2020)
• Publication Date: 2020-09-29
• Subjects: Radiology -- Big Data -- AI -- ML; Information storage and retrieval systems -- Research -- Periodicals; Biology -- Research -- Periodicals; Medical sciences -- Research -- Periodicals; Database management -- Periodicals; 570.285
• DOI: 10.1093/gigascience/giaa095
• Languages: English
• ISSNs: 2047-217X

Abstract

Abstract: Aim: To enable a world-leading research dataset of routinely collected clinical images linked to other routinely collected data from the whole Scottish national population. This includes more than 30 million different radiological examinations from a population of 5.4 million and >2 PB of data collected since 2010. Methods: Scotland has a central archive of radiological data used to directly provide clinical care to patients. We have developed an architecture and platform to securely extract a copy of those data, link it to other clinical or social datasets, remove personal data to protect privacy, and make the resulting data available to researchers in a controlled Safe Haven environment. Results: An extensive software platform has been developed to host, extract, and link data from cohorts to answer research questions. The platform has been tested on 5 different test cases and is currently being further enhanced to support 3 exemplar research projects. Conclusions: The data available are from a range of radiological modalities and scanner types and were collected under different environmental conditions. These real-world, heterogenous data are valuable for training algorithms to support clinical decision making, especially for deep learning where large data volumes are required. The resource is now available for international research access. The platform and data can support new health research using artificial intelligence and machine learning technologies, as well as enabling discovery science.