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AI and new imaging database will speed up diagnosis of COVID

AI
18th January 2021 about a 5 minute read
“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.” Carola-Bibiane Schönlieb, University of Cambridge Professor of Applied Mathematics and head of the Cambridge Image Analysis group

Patients with COVID-19 are set to benefit from faster treatment, improved outcomes and shorter hospital stays thanks to greater use of the latest artificial intelligence.

Access to an AI imaging database has been extended to hospitals and universities across the country to speed up diagnosis of COVID-19.

NHSX, the unit tasked with driving the digital transformation of care in the NHS, has brought together over 40,000 CT scans, MRIs and X-rays from more than 10,000 patients across the UK during the course of the pandemic.

Hospitals and universities are now able to use the National COVID-19 Chest Imaging Database (NCCID) to track patterns and markers of illness. The database can speed up diagnosis of COVID-19, leading to a quick treatment plan and greater understanding of whether the patient may end up in a critical condition.

Meanwhile the NHS AI Lab has published A guide to good practice for digital and data-driven health technologies, setting out what the NHS is looking for when it buys digital and data-driven technology for use in health and care.

Matt Hancock, Secretary of State for Health and Social Care, said:

“The use of artificial intelligence is already beginning to transform patient care by making the NHS a more predictive, preventive and personalised health and care service.

“It is vital we always search for new ways to improve care, especially as we fight the pandemic with the recovery beyond. This excellent work is testament to how technology can help to save lives in the UK.”

Clinicians at Addenbrooke’s Hospital in Cambridge are developing an algorithm based on the NCCID images to help inform a more accurate diagnosis of patients when they present to hospital with potential COVID-19 symptoms and have not yet had a confirmed test. 

Visual virus signatures

Using visual signatures of the virus, as they appear in chest scans, they are able to compare the patterns in the patient’s imaging with those seen previously in the NCCID to get a more accurate diagnosis and prognosis.

Carola-Bibiane Schönlieb, University of Cambridge Professor of Applied Mathematics and head of the Cambridge Image Analysis group, said:

“The ability to access the data for 18 different trusts centrally has increased our efficiency and ensures we can focus most of our time on designing and implementing the 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.”

The NCCID is also helping researchers from universities in London (University College London), and Bradford, to develop AI tools that could help doctors improve the treatment for patients with COVID-19.

National AI imaging platform

The database is helping to inform the development of a potential national AI imaging platform to safely collect and share data, developing AI technologies to address a number of other conditions such as heart disease and cancers.

The NHS AI Lab has also set up and launched a £140 million AI award in collaboration with the Accelerated Access Collaborative (AAC) and National Institute for Health Research (NIHR). Initial bids were awarded to 42 organisations in September 2020 with a further round of bids closing last month.

Dominic Cushnan, Head of AI Imaging at NHSX, said: “We are applying the power of artificial intelligence to quickly detect disease patterns and develop new treatments for patients. There is huge potential for patient care, whether through quicker analysis of chest images or better identification of abnormalities.

“The industrial scale collaboration of the NHS, research and innovators on this project alone has demonstrated the huge potential and benefits of technology in transforming care”.

Dr Mark Halling-Brown, Head of Scientific Computing at Royal Surrey County Hospital, said his trust had “led the way in creating and sharing research imaging databases that have enabled the development of AI tools.

“More recently we’ve specialised in the evaluation and validation of AI radiology products within a range of specialties supporting their safe deployment into the clinic”.

Background information

Led by NHSX, the NCCID is a collaborative effort with the British Society of Thoracic Imaging (BSTI), Royal Surrey NHS Foundation Trust and Faculty, a London-based AI specialist.

All scans in the library are stripped of any identifying patient details by each hospital trust before they are submitted to the national collection, ensuring researchers are only able to access pseudonymised information.

More information about the NCCID, including a full list of research projects, is available here

The team are encouraging more trusts to submit scans to add to the database.  The NHS trusts who have taken part so far include:

  • Royal United Hospitals Bath NHS Foundation Trust
  • Brighton and Sussex University Hospitals NHS Trust
  • London North West University Healthcare NHS Trust
  • George Eliot Hospital NHS Trust
  • Cwm Taf Morgannwg University Health Board
  • Hampshire Hospitals NHS Foundation Trust
  • Betsi Cadwaladr University Health Board
  • Ashford and St Peter’s Hospitals
  • Royal Cornwall Hospitals NHS Trust
  • 10.Sheffield Children’s NHS Foundation Trust
  • 11.Liverpool Heart and Chest Hospital NHS Foundation Trust
  • 12.Norfolk and Norwich University Hospitals NHS Foundation Trust
  • 13.Royal Surrey NHS Foundation Trust
  • 14.Sandwell and West Birmingham NHS Trust
  • 15.West Suffolk NHS Foundation Trust
  • 16.Somerset NHS Foundation Trust
  • 17.Cambridge University Hospitals NHS Foundation Trust
  • 18.Imperial College Healthcare NHS Trust
  • 19.Oxford University Hospitals NHS Foundation Trust
  • 20.Sheffield Teaching Hospitals NHS Foundation Trust

A list of the AI in Health and Care Award winners is available here