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Vets use machine-learning to diagnose Cushing's in dogs
Researchers at the RVC have shown how machine-learning technology can be used to improve diagnostics in practice.

Technology could be harnessed to improve diagnostics in practice

Researchers at the Royal Veterinary College (RVC) have used machine-learning technology to diagnose Cushing’s syndrome in dogs.

One of the most common endocrine diseases of dogs, Cushing’s syndrome can increase the risk of diabetes mellitus, hypertension and pancreatitis. Treatment, therefore, relies on a fast and accurate diagnosis, but this can be difficult to achieve based on clinical symptoms alone. 

Vets have traditionally diagnosed Cushing’s through multiple blood tests - but this is a costly, lengthy process that is not always accurate. 

In a bid to address this problem, researchers within the RVC’s VetCompass programme, supported by Dechra Ltd, anonymised data from 939 dogs tested for Cushing's at practices across the UK.

The team used machine-learning algorithms to assess the dogs’ clinical records, including demographics, clinical signs at presentation and laboratory results. Dogs suspected of having Cushing’s were also inlcuded in the analysis.

Their findings, published in Scientific Reports, suggest that machine-learning technology could predict the diagnosis of a practising vet and may also contribute to improved diagnosis of the disease. 

Imogen Schofield, lead author and PhD student at the RVC, said the development of the algorithms could lead to more reliable and cost-effective care for dogs with Cushing’s disease - and may also help in the diagnosis and treatment of other clinical problems. 

“Machine-learning algorithms, like those used in this study, are already widely integrated in our day-to-day lives to help make certain decisions, such as Google or Netflix recommendations. Now this technology can be harnessed to help improve diagnostics in veterinary practice,” she explained.

“By embracing the use of machine-learning methods, we are a step closer to providing vets in primary-care practice with an easy to use, low cost and accurate test that can support the often frustrating process of diagnosing Cushing’s syndrome in dogs."

Greg Williams, senior business manager at Dechra Ltd. and industrial supervisor of the PhD studentship, said: “As experts in Endocrinology, our constant endeavour is to strive for better and earlier diagnosis and treatment of pets with endocrine diseases. By funding Imogen's PhD and working with the RVC we have been able to develop validated clinical scoring and quality-of-life assessments to help vets deliver effective control and management of Cushing's syndrome in dogs. 

“In addition, this innovative development of the machine-learning-based prediction of Cushing’s syndrome means we have the potential to further support and improve vets' ability to diagnose Cushing’s, and thereby improve a dog’s quality-of-life, and as a result their owner’s quality-of-life.”

Image (C) Imogen Schofield.

 

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Practices urged to audit neutering data

News Story 1
 RCVS Knowledge has called on vet practices to audit their post-operative neutering outcomes.

It follows the release of the 2024 NASAN benchmarking report, which collates data from neutering procedures performed on dogs, cats and rabbits.

The benchmarking report enables practices in the UK and Ireland to compare their post-operative outcomes to the national average. This includes the rate of patients lost to follow-up, which in 2024 increased to 23 per cent.

Anyone from the practice can submit the data using a free template. The deadline for next report is February 2026.

Visit the RCVS Knowledge website to complete an audit. 

Click here for more...
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The guidance is free and has been produced to help veterinary practitioners select the most appropriate antibiotic for rabbits. It covers active substance, dose and route of administration all of which are crucial factors when treating rabbits owing to the risk of enterotoxaemia.

For more information and to access the guide, visit the BSAVALibrary.