Tag Archives: AI

Greyhound racing says it’s transparent, so we used AI to check – dog by dog

When an industry publishes its own welfare data, how can anyone check it? We built AI agents to go through the public records on fatalities in greyhound racing and found a rising death rate

By Dr Mia Cobb and Dr Simon Coghlan, University of Melbourne

When we saw data published by a greyhound racing regulator in the UK, something about the dogs didn’t add up.

According to their report, the rate of dog deaths in races from 2022 to 2024 was stable. However, that number had actually risen from 99 to 123, while the number of races had fallen over the same period.

The maths was not mathing.

The fatality rate (which is calculated as the number of dogs that died on the track while racing, divided by the total number of individual dog runs, multiplied by 100 to express it as a percentage) was presented as 0.03 per cent across three consecutive years.

But when we ran the sums and reported them to an extra decimal place, we saw the fatality rate had risen by 30 per cent.

By reporting this information to only two decimal places, the increase in dogs dying in races was masked.

So, when an industry that relies on animals publishes welfare data, how can the public – or the policymakers making decisions about that industry’s future – know if the headline figures portray the real situation?

In the UK, there is a regulated industry with public-facing records, a governing body that publishes welfare data and a long-running debate about whether that data tells the full story.

For researchers interested in animal welfare, the UK greyhound industry also presents a genuine test case for a new method.

In fact, the question we wanted to answer was not specific to greyhound racing, it was broader.

When welfare-relevant information exists across multiple public sources but has never been systematically assembled, can AI agents do that work reliably, ethically, transparently and at scale? If so, what can we learn that the industry’s reporting doesn’t tell us?

Building our greyhound data set

The Greyhound Board of Great Britain (GBGB) is the governing body that licenses and regulates commercial greyhound racing in the UK.

Falls affect one in six racing greyhounds. Picture: Getty Images

It holds detailed records on every dog registered there, including country of origin, racing history, injuries, destination when racing ends and reason for leaving racing.

While some of this information is publicly visible, a good deal of it is not.

We made six requests for access to GBGB data, but none gave us access to the data we needed. We were told that most of what we wanted was already on their website. It wasn’t – at least not in any form that allowed independent analysis.

So, we built the dataset ourselves.

Using AI agents – software that performs repetitive tasks under continuous human supervision – we pulled together information from several public websites to uncover animal welfare insights for 31,028 greyhounds that raced in licensed UK competitions. 

That’s around 1.27 million race starts across 22 licensed tracks between January 2022 and March 2026.

We gathered data from five public registries, and by cross-referencing these sources, we were able to get a relatively complete view of the whole population, not just a sample.

Thanks to the AI agents, what would have taken months of manual research was completed in days.

What the data tells us about greyhound racing

As we went through the data, we began to see things that the industry’s transparency has not previously revealed.

The typical greyhound’s racing career is 30 starts over 11.9 months. Most dogs race for less than a year.

More than 85 per cent of greyhounds racing in the UK were bred in Ireland.

The typical greyhound’s racing career is 30 starts over 11.9 months. Picture: Getty Images

This matters because dog breeding and rearing in Ireland falls outside the jurisdiction of the British system which is later responsible for their welfare.

One in four racing dogs – around 24 per cent – experienced at least one adverse event (including injuries or fatalities) during our observation period, with falls affecting one in six dogs.

These ‘adverse events’ are recorded by track officials at the race but have not been made available to the public at a track level, even when the Scottish and Welsh parliamentary committees asked for it.

The GBGB publishes one annual list of aggregated injury figures that covers all licensed tracks.

This has meant that the public, researchers, regulators and even greyhound trainers have not been able to independently check whether conditions at one track carry greater risk.

Our data finds that some tracks are five times more likely to result in adverse events for dogs than other venues.

Where our trail goes cold

Of the 31,028 dogs in our dataset, 73 per cent had left GBGB-regulated racing by the end of March 2026.

The GBGB publishes an aggregate of annual figures on retirement destinations. It reported that 94 per cent of dogs leaving the industry in 2024 were rehomed or ‘retained by trainers’.

But, importantly, the wellbeing of these dogs cannot be independently verified because it falls outside the registered racing regulations.

Over the last year, parliaments in New Zealand, Wales and Scotland have all decided to ban greyhound racing. Picture: Getty Images

Why this matters beyond greyhounds

An industry’s social license to operate depends on public trust. In an era of growing concern for animal welfare, that trust will increasingly require verifiable evidence.

The GBGB is not unusual in its approach to information disclosure.

Across animal-reliant industries, data tends to flow inward to self-governing bodies, and is only released outward in tightly controlled formats.

Independent researchers like us face fragmented public registries, opaque systems and, when we ask directly, deflection or refusal.

What our study shows is that technology can sometimes help overcome this information imbalance. AI agents, applied carefully and with human oversight, can compile population-level welfare datasets from publicly accessible sources, at a scale and speed that makes independent scrutiny genuinely and ethically feasible.

It does not give us the data the GBGB holds privately. Nothing can do that, short of the governing body releasing it.

Over the last year, parliaments in New Zealand, Wales and Scotland have all decided to ban greyhound racing.  

Here in Australia, Tasmania may follow. Western Australia is mid-way through a formal inquiry. South Australia’s greyhound racing industry faces a government-imposed reform deadline in July 2026.

In each of these places, the same question is being asked: how do we know the welfare assurance offered by the industry is real?

For the first time, we can describe the dogs racing in the UK in detail. It’s making these animals visible.

How do we know the welfare assurance offered by the industry is real? Picture: Waggles Photography

We know things we didn’t know before.

More than half of the greyhounds are black. There are as many females as males. Most of them are bred in Ireland. They start racing at around 21 months. And within a year of their first race, most have disappeared from public view.

Whether this visibility is used to hold the industry to account is a separate question.

Source: This article was first published on Pursuit. Read the original article.

The article has been republished on this blog thanks to a Creative Commons Attribution No Derivatives 4.0 International license

Novel, targeted canine cancer therapy receives US patent

Photo: Maria Sbytova/Adobe Stock

FidoCure, the flagship brand of the One Health Company, has been granted a patent for a novel targeted therapy and biomarker for canine cancer by the US Patent and Trademark Office (USPTO). The patent covers a new approach to treatment, using mutation profiling to better manage bladder cancer in dogs.1

The now-patented approach, driven by artificial intelligence (AI), considers real-world evidence from veterinary clinical data and uses it to determine the ideal methodology for therapeutic treatment. Early data reports suggest the new approach may be more efficacious compared to conventional treatment methods.1

“This patent acknowledges the uniqueness of our approach, which has consistently demonstrated that it can transform outcomes for pet dogs with cancer while improving the quality of life for the patients,” Christina Lopes, cofounder and CEO of the One Health Company and FidoCure, said in a news release.1 “Receiving a patent is an important milestone in our mission to increase access to lifesaving treatments for pet dogs with cancer.”

Each year, 6 million dogs in the US are affected by cancer, and many of them by bladder cancer.1 Transitional cell carcinoma (TCC) is the tumor that affects the bladder, and Atlantic Veterinary Internal Medicine and Oncology (AVIMO) approximates that TCC is diagnosed in 80,000 dogs each year.2 Biologically speaking, canine cancer is relatively similar to human cancer, but, according to FidoCure, treatment and care for canine cancer is about 20 years behind humans.1

“Bladder cancer [treatment] in dogs is an unmet need,” Gerald Post, DVM, MEM, CACVIM, chief medical officer of One Health, said in a news release.1 “Traditional chemotherapy and radiation therapy are often ineffective, cause toxic side effects, and are expensive.”

The patent, No. 12036281-B2, protects FidoCure’s technology for 20 years following its priority date.1 Lopes, alongside her FidoCure cofounder Benjamin Lewis and other key team members, are named as inventors on the patent. The company currently has an additional 8 pending patents, covering novel therapeutics and biomarkers, and will be seeking approval from the FDA for their products in the near future, according to the release.1

FidoCure came together with the goal to improve outcomes for canine cancer patients. More specifically, the founders wanted to put an end to blanket chemotherapy and radiation treatments, regardless of cancer type, citing the use of target therapies in human oncology. They’ve since partnered with a board of medical and veterinary advisors with multidisciplinary knowledge, spanning the fields of human and canine oncology.3

Among the company’s achievements is the FidoCure Next Generation Sequencing Test, through which the results allow care providers to better customize treatment options using targeted therapies. In early 2022, FidoCure partnered with IDEXX, a company focused on global pet healthcare innovation, to provide access to the test to its’ veterinary clients throughout the US and Canada.4

References

  1. FidoCure Receives Patent for AI-Driven Drug Development, Accelerating a Novel Portfolio of Precision Therapeutic for Cancer. News release. FidoCure. August 13, 2024. Accessed August 15, 2024. https://www.businesswire.com/news/home/20240813574729/en/FidoCure-Receives-Patent-for-AI-Driven-Drug-Development-Accelerating-a-Novel-Portfolio-of-Precision-Therapeutic-for-Cancer
  2. Understanding Bladder Cancer in Dogs. American Veterinary Internal Medicine and Oncology. Accessed August 15, 2024. https://avim.us/bladder-cancer-in-dogs/
  3. FidoCure. AI Driven Precision Medicine Platform for Canine Cancer. One Health Company. Accessed August 15, 2024. https://fidocure.com/
  4. One Health Company partners with IDEXX to enhance canine cancer care. dvm360. January 20, 2022. Accessed August 23, 2024. https://www.dvm360.com/view/one-health-company-partners-with-idexx-to-enhance-canine-cancer-care

Source: MJH Life Sciences, DVM 360

AI could help diagnose dogs suffering from chronic pain

A new artificial intelligence (AI) technique developed by the University of Surrey could eventually help veterinarians quickly identify Cavalier King Charles Spaniel (CKCS) dogs with a chronic disease that causes crippling pain. The same technique identified unique biomarkers which inspired further research into the facial changes in dogs affected by Chiari-like malformation (CM).

CKCS

Photo by Getty Images

Cavalier King Charles Spaniels are predisposed to CM – a disease which causes deformity of the skull, the neck (cranial cervical vertebrae) and, in some extreme cases, lead to spinal cord damage called syringomyelia (SM). While SM is straightforward to diagnose, pain associated with CM is challenging to confirm and why this research is innovative.

In a paper published by the Journal of Veterinary Internal Medicine, researchers from Surrey’s Centre for Vision, Speech and Signal Processing (CVSSP) and School of Veterinary Medicine (SVM) detail how they used a completely automated, image mapping method to discover patterns in MRI data that could help vets identify dogs that suffer from CM associated pain. The research helped identify features that characterise the differences in the MRI images of dogs with clinical signs of pain associated with CM and those with syringomyelia from healthy dogs. The AI identified the floor of the third ventricle and its close neural tissue, and the region in the sphenoid bone as biomarkers for pain associated with CM and the presphenoid bone and the region between the soft palate and the tongue for SM.

Dr Michaela Spiteri, lead author of the study from CVSSP, said: “The success of our technique suggests machine learning can be developed as a diagnostic tool to help treat Cavalier King Charles Spaniel’s that are suffering from this enigmatic and terrible disease. We believe that AI can be a useful tool for veterinarians caring for our four-legged family members.”

Identification of these biomarkers inspired a further study, also published in the Journal of Veterinary Internal Medicine, which found that dogs with pain associated with CM had more brachycephalic features (having a relatively broad, short skull) with reduction of nasal tissue and a well-defined stop.

SVM student, Eleonore Dumas, whose 3rd year project formed part of the study data, said: “Being able to contribute to the development of diagnostic tools that allow for earlier diagnosis of patients suffering from this painful condition has been both challenging and incredibly rewarding.”

Dr Penny Knowler, lead author of the study from SVM, said: “This study suggests that the whole skull, rather than just the hindbrain, should be analysed in diagnostic tests. It also impacts on how we should interpret MRI from affected dogs and the choices we make when we breed predisposed dogs and develop breeding recommendations.”

Adrian Hilton, Distinguished Professor from the University of Surrey and Director of CVSSP, said: “This project demonstrates the potential for AI using machine learning to provide new diagnostic tools for animal health. Collaboration between experts in CVSSP and Surrey’s School of Veterinary Medicine is pioneering new approaches to improve animal health and welfare.”

Both studies were funded by the Memory of Hannah Hasty Research Fund. Hannah was a CKCS unaffected by CM/ SM and a much beloved companion, giving her owner much support and joy. The AI study was also supported by the Pet Plan Charitable Trust.

The findings of the studies are available to read on the Journal of Veterinary Internal Medicine website here and here.