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News Article

Machine learning techniques assist Chiari‐like malformation diagnosis

An artificial intelligence technique has been developed to characterize morphological changes in dogs that may or may not be apparent to human observers

Cavalier King Charles Spaniel (CKCS) are predisposed to Chiari-like malformation (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.

In a paper in the Journal of Veterinary Internal Medicine, researchers from the University of Surrey’s Centre for Vision, Speech and Signal Processing (CVSSP) and School of Veterinary Medicine (SVM) detail how they used an 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 artificial intelligence (AI) technique 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 with reduction of nasal tissue and a well-defined stop.

SVM student, Eleonore Dumas, whose third-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, 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.”


Spiteri, M., Knowler, S.P., Rusbridge, C., Wells, K. (2019). Using machine learning to understand neuromorphological change and image‐based biomarker identification in Cavalier King Charles Spaniels with Chiari‐like malformation‐associated pain and syringomyelia. Journal of Veterinary Internal Medicine, Early View, online 24 September 2019, doi: 10.1111/jvim.15621

Knowler, S.P., Dumas, E., Spiteri, M., McFadyen, A.K., Stringer, F., Wells, K., Rusbridge, C. (2019). Facial changes related to brachycephaly in Cavalier King Charles Spaniels with Chiari‐like malformation associated pain and secondary syringomyelia. Journal of Veterinary Internal Medicine, Early View, online 5 November 2019, doi: 10.1111/jvim.15632

Article details

  • Date
  • 08 November 2019
  • Source
  • University of Surrey
  • Subject(s)
  • Dogs, Cats, and other Companion Animals