Award Title: Novel Applications and Implementation of Artificial Intelligence in Paediatric Radiology
Funder: Department of Health and Social Care

Abstract: RESEARCH QUESTIONCan an artificial intelligence (AI) algorithm improve decision making in children's fracture detection?BACKGROUNDLimb fractures are the most common traumatic injuries in children. Their detection can be missed leading to pain, discomfort, poor healing and missed treatment opportunities. In some cases, they may be the first marker of child abuse, and missed opportunities for safe-guarding can be fatal. Immediate paediatric radiological opinions are difficult to access nationwide, however an AI algorithm trained on thousands of children's radiographs could offer an immediate and potentially more accurate method of detection, helping to improve patient outcomes through efficiency and reduced errors. AIMSTo develop an AI algorithm capable of detecting paediatric limb fractures, test the diagnostic accuracy of this tool compared to healthcare professionals, and determine whether this could be implemented in clinical care to reduce errors and improve emergency physician confidence in fracture detection.HYPOTHESISAn AI algorithm can be developed for accurate paediatric fracture detection, with performance equivalent to human paediatric radiologist standard for use in an emergency care triage setting. METHODS & TIMELINEThis study will be conducted in several phases over 5 years.WP1 (0-12 months): Stakeholder engagementA survey of parent and paediatric patients attending the radiology department will be conducted, asking how they feel AI should be used in clinical practice, and how they wish to be informed of doctors using these tools. WP2 & 3 (6 - 48 months): Algorithm development and validationCuration and annotation of a large, multi-centre, retrospective dataset of 4 types of paediatric limb radiographs will be undertaken, using double read expert consensus reports by paediatric radiologists as a reference standard. An AI algorithm will be developed and validated on this dataset, before being tested on an independent prospective external dataset. The AI diagnostic accuracy from this external test set will be compared against those of paediatric radiologists. WP4 (48 -h; 60 months): Simulated implementation The algorithm will be tested to see how it might perform in clinical practice through a simulated study. An online multi-reader study of stakeholders (emergency care physicians and consultant nurses) will review radiographs with and without using the AI algorithm to evaluate whether the use of AI leads to changes in diagnosis, reduced errors, different management plans or an increased confidence in diagnosis.IMPACT & DISSEMINATION The immediate scientific and clinical impact of this research would be better fracture care in children. This AI development pipeline (surveying parents and children, developing and validating the AI algorithm, with simulated clinical implementation) will become the standard blueprint method for pre-implementation AI development in the NHS. Guided by PPI, this research will feed into the next phase of regulatory approvals for nationwide implementation and evaluation, where those children most at need will get rapid access to this technology.

Lay Abstract: WHAT IS THE PROBLEM?Half of all the 12 million children ( There are three problems with identifying children's fractures:	Growing bones can look like fractures. 	Children have different types of fractures than adults (e.g. buckle fractures) 	Fractures in children are not always obviousWithout an expert opinion from a radiologist (a specialist who looks at medical images), mistakes are made in diagnosing 10% of children's fractures by emergency doctors. Unfortunately, we don't have enough specialist children's radiologists in the NHS to check all X-rays immediately, meaning a delay in recognising mistakes. If fractures are missed, children will be left in pain and won't get the right treatment, leading to long term problems with discomfort and disability. In some cases, a fracture can be a sign of abuse, and crucial opportunities to arrange for the child's safety are missed.WHAT CAN BE DONE?Artificial intelligence (AI) has the potential to be used to find fractures on children's X-rays as accurately as a radiologist. AI is a computer programme trained to carry out actions usually done by humans - in this case to find fractures on X-rays. This could reduce the number of missed fractures, ensure children get the right treatment quickly, reduce inefficiencies for parents and children, and save money for the NHS.HOW AND WHEN CAN THIS BE DONE?Step 1 (Year 1): A large survey of children and parents attending hospital will be carried out, asking their views about using AI for diagnosis and how best to introduce this in the NHS, helping us make our programme as acceptable to patients as possible.Step 2 (Year 2-3): A computer programme (using AI) will be developed to 'learn' which X-rays are normal and abnormal, using thousands of examples of X-rays from children who visited hospitals in the UK over the last 5 years. Step 3 (Year 4): The programme will be tested on new X-rays to see how many mistakes it makes, compared to radiologists. Step 4 (Year 5): The programme will be tested again to see if it would work in real life: how it would change the way emergency department doctors would care for a child and if it reduces mistakes, improves doctors' confidence in decision making, and is easy to understand. At this point, the programme could start to be made freely available across the NHS. HOW ARE PATIENTS INVOLVED?The Great Ormond Street Hospital (GOSH) Young Persons Advisory Group (YPAG) -h; a group of young people interested in medicine and research - and their parents have helped design this study. Four members have volunteered to join a Steering Group for this specific study. They will co-design patient surveys, make sure information about AI can be explained to patients in a user-friendly way and help share our results on websites and social media. HOW WILL THIS HELP PATIENTS?If this programme works well, it could be easily installed in every hospital throughout the NHS, helping doctors make the right decisions. More children's fractures will be correctly managed, quicker and more efficiently, ensuring all children get the right treatment, saving money for the NHS.This series of steps (asking patients and parents, developing a programme, testing it to see if it works, then checking that it works in practice) could become the standard way to develop AI programmes in the NHS, and used as an example for others to follow.

Research Activity Codes: 4.2 EVALUATION OF MARKERS AND TECHNOLOGIES
Health Categories: MUSCULOSKELETAL;INJURIES AND ACCIDENTS