Research aims to use data gathered by veterinary practices to better predict which dogs are at risk of Cushing’s and start on early treatment.
Researchers at the RVC have used an AI learning algorithm to better diagnose Cushing’s syndrome in dogs.
Vets at the RVC have been looking for ways to diagnose the disease in dogs as early as possible, and have been using AI to examine a sample of 905,554 dogs and 886 veterinary practices across the UK.
Early treatment
Currently, vets are only able to reach a diagnosis of Cushing’s syndrome through a series of cost-ineffective blood tests.
The research, funded by veterinary pharmaceutical company Dechra, aims to use data gathered by clinics to better predict which dogs are at risk of Cushing’s and start on early treatment.
Machine learning
Imogen Schofield, lead author and PhD student at the RVC, said: “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.”
Clinical scoring
Greg Williams, senior business manager at Dechra 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.”
Further support
Mr Williams added: “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.”
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