AI Could Help Diagnose Dogs Suffering From Chronic Pain and Identify Facial Changes Associated with Chiari-like Malformation
Surrey, England, UK — A new artificial intelligence (AI) technique developed by the University of Surrey could eventually help veterinarians to identify Cavalier King Charles Spaniel (CKCS) dogs with Chiari-like malformation, a chronic disease that causes crippling pain. The same technique identified unique biomarkers which inspired additional research into the facial changes in dogs affected by Chiari-like malformation (CM).
CKCS are predisposed to Chiari-like malformation – 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.
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 characterize 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.
“The success of our technique suggests machine learning can be developed as a diagnostic tool to help treat Cavalier King Charles Spaniels that are suffering from this enigmatic and terrible disease,” Dr. Michaela Spiteri, lead author of the study from CVSSP, said in a press release. “We believe that AI can be a useful tool for veterinarians caring for our four-legged family members.”
Identification of these biomarkers inspired a second 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.
“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,” said SVM student, Eleonore Dumas, whose 3rd year project with the Veterinary Health Innovation Engine (vHive), a research centre based at the Surrey Vet School, formed part of the study data.
“This study suggests that the whole skull, rather than just the hindbrain, should be analysed in diagnostic tests,” said Dr. Penny Knowler, lead author of the study from SVM. “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.”
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Press release provided by the University of Surrey. Content may have been edited for style and length.