RacingBetter News
	Wednesday 25th February 2026

Royal Veterinary College Study Reveals how AI could Transform Fracture Detection in Animals

Research from the Royal Veterinary College (RVC) has been shortlisted for the prestigious STEM for Britain 2026 award in recognition of its work applying artificial intelligence (AI) to improve fracture detection in animals. By successfully applying an advanced AI system to equine fracture cases and evidencing its cross-species capability, the findings identify opportunities to strengthen diagnostic accuracy and efficiency in veterinary practice and support animal welfare.

Fractures are a leading cause of injury in Thoroughbred racehorses, significantly impacting both their welfare and racing careers. Approximately 10% of racehorses sustain a fracture during training, with bone injuries occurring at a rate of 1.3 per 1000 starts in flat racing. However, despite their impact, fractures can be difficult to diagnose. Assessment relies largely on radiographs, yet variations in image quality and projection, combined with the difficulty of identifying subtle bone changes, can limit diagnostic accuracy.

Improving early and reliable fracture detection is therefore critical to support horse welfare and prevent fatal injuries. This study by the RVC marks the first step in a longer-term research programme focused on identifying early bone changes before they progress to serious or career-ending fractures.

Led by Ruby Chang, Associate Professor of Statistics at the RVC, the study was carried out by Dr Hanya Ahmed. The team compiled a databank of images, including 100 equine fracture cases from two UK equine hospitals and published literature; 70 feline cases from hospital databases; and approximately 4000 human fracture images from a public database.

Using this combination of images, the researchers built an AI system that works in three stages. First, it identifies the type of scan, such as an X-ray, CT or MRI, then it recognises the image angle, before detecting and precisely locating any fractures.

The study revealed that the AI system was able to detect and localise fractures in horses using knowledge gained from thousands of human fracture images. This approach, known as “transfer learning”, enabled the model to be trained on a large human dataset before being adapted for veterinary use. As a result, the system achieved fracture localisation accuracy of between 71 and 84% without requiring an unrealistically large number of equine images.

The findings demonstrate the potential for AI-assisted tools to strengthen fracture diagnosis across veterinary practice. Faster and more reliable detection could help reduce uncertainty in clinical decision-making and enable earlier treatment, with clear benefits for the welfare and recovery of racehorses and companion animals. More broadly, the study shows how advances in AI developed for human medicine can be successfully adapted for animal health and help to deliver safer and more consistent care across species.

Building on this work, the team has expanded its collaboration with the Hong Kong Jockey Club to explore whether AI can identify early bone changes in racehorses before a fracture occurs. If successful, this approach could support efforts to prevent catastrophic injuries, marking an important step toward using AI to not only diagnose, but to help prevent fractures before they happen.

Dr Ruby Chang, Associate Professor of Statistics at the RVC, said:

“I am delighted that research from our team, led by the outstanding work of Dr Hanya Ahmed, has been selected as a finalist for the prestigious STEM for Britain 2026. Dr Ahmed has brilliantly translated expertise in medical image analysis to the veterinary field, developing a novel AI system to detect fractures in racehorses. This exceptional work has now also been published in Bioengineering. This dual recognition is a testament to Dr Ahmed's skill and dedication, and a wonderful celebration of our team's collaborative effort to advance diagnostic technology.”

This research was funded by the Horserace Betting Levy Board.