A consortium comprising of 13 trusts across Cheshire and Merseyside has worked with Royal Philips to create a regional hub for the National COVID-19 Chest Imaging Database (NCCID).
Launched in January 2021, the regional hub has been used across the organisations to connect to the NCCID. The database is part of a programme nationally to support the validation of AI products for use in the NHS, and also by researchers, clinicians and technology companies wanting to investigate the disease and develop solutions that can support the COVID-19 patient care pathway.
Prof Mark-Halling Brown, Head of Scientific Computing at Royal Surrey NHS Foundation Trust, said: “One of the findings coming out of the end of this project will be to focus on regional hubs that will be able to coordinate and better centralise the data, a hub just like Cheshire and Merseyside.
“It can take many months or even years to set up solutions at individual trusts, so doing it regionally is the only way to scale up nationally.”
Steve Sparks, Professional Services Manager RI at Philips UK&I, added: “Most District General Hospitals average 250,000 to 350,000 imaging exams per year, with a single regional solution we have been able to gain access to the images from the 13 trusts within the Cheshire & Merseyside consortium.”
The SMART box technology deployed for the Cheshire and Merseyside consortium aims to support research into areas requiring large volumes of de-identified clinical imaging data. The NCCID is one work-stream by the NHS AI Lab at NHSX, hoping to accelerate the safe, ethical, and effective adoption of AI in health and care.
Carola-Bibiane Schönlieb, Professor of Applied Mathematics and head of the Cambridge Image Analysis group at the University of Cambridge, commented on their use of the database: “The NCCID has been invaluable in accelerating our research and provided us with a diverse, well-curated, dataset of UK patients to use in our algorithm development.”
“The ability to access the data centrally has increased our efficiency and ensures we can focus most of our time on designing and implementing algorithms for use in the clinic for the benefit of patients.
“By understanding in the early stages of disease, whether a patient is likely to deteriorate, we can intervene earlier to change the course of their disease and potentially save lives as a result.”