Artificial intelligence-supported early fracture diagnosis
Research type
Research Study
Full title
Artificial intelligence-supported early fracture diagnosis
IRAS ID
271600
Contact name
Corri Black
Contact email
Sponsor organisation
University of Aberdeen
Clinicaltrials.gov Identifier
DaSH330, DaSH safe haven number
Duration of Study in the UK
1 years, 0 months, 2 days
Research summary
Each year in Scotland, the NHS gives some 5,000 patients x-rays of wrists, hands, ankles and feet, most often looking for a fracture after trauma. Although isolated injuries in these areas are often categorised as ‘minor’, misdiagnosis and consequent mismanagement can result in significant impact for patients and financial costs to the NHS.
Artificial Intelligence (AI) or “machine learning” (a set of procedure rules to take in clinical data such as X-rays, assess the risk of a fracture and present this risk and information to a clinical team) could be developed to help clinicians make diagnoses.
To develop AI or machine learning tools and to take these tools to the level of “approved for health care use” and integrated into the appropriate IT and/or equipment for healthcare use requires a partnership between NHS, academia and industry.
NHS Grampian A&E and Radiology clinicians have identified that there is significant clinical need and are eager to work in partnership with those with the technical skills to develop potential solutions.
This project seeks to take the first step by creating a small (100 patients) fully anonymised x-ray dataset (no patient names, addresses, date of birth or hospital numbers); creating an AI laboratory space within our local accredited secure data safe haven; enabling up to 5 NHS/Industry/Academic partnerships to securely access the data to see if they show potential to develop useful clinical solutions to support fracture detection.
In a second phase, successful partnerships will be invited to apply for permission to access to a larger dataset to develop their solutions further.
REC name
East of Scotland Research Ethics Service REC 1
REC reference
19/ES/0117
Date of REC Opinion
30 Sep 2019
REC opinion
Favourable Opinion