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ORIGINAL RESEARCH article

Front. Vet. Sci.
Sec. Veterinary Neurology and Neurosurgery
Volume 11 - 2024 | doi: 10.3389/fvets.2024.1492259

A Data Driven Approach for Soft Tissue Biomarker Identification Linked to Chiari-like Malformation and Syringomyelia

Provisionally accepted
Jake Cumber Jake Cumber 1*Emma Scales-Theobald Emma Scales-Theobald 2Clare Rusbridge Clare Rusbridge 2,3,4KEVIN WELLS KEVIN WELLS 1
  • 1 Centre for Vision, Speech and Signal Processing, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, Surrey, United Kingdom
  • 2 School of Veterinary Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, England, United Kingdom
  • 3 Wear Referrals Veterinary Specialist & Emergency Hospital, Bradbury, United Kingdom
  • 4 Fitzpatrick Referrals, Guildford, Surrey, United Kingdom

The final, formatted version of the article will be published soon.

    Canine Chiari-like malformation (CM) is a neuroanatomical condition associated with conformational change of the cranium, craniocervical junction and neuroparenchyma, resulting in pain (Chiari associated pain or CM-P) and the development of syringomyelia (SM). The associated neuro-disability in affected individuals compromises quality of life. CM is characterised by overcrowding of the brain and cervical spinal cord and is predisposed by skull-base shortening and miniaturisation with brachycephalic toy dogs overwhelmingly represented. Magnetic resonance imaging (MRI) is conventionally used for diagnosis; however, CM is complex and ubiquitous in some dog breeds so that diagnosis of CM-P relies on a combination of clinical signs, MRI, and elimination of other causes of pain. This research aimed to identify cranial and spinal pathologies and neural morphologies linked to CM-P and SM in dogs using MRI scans and machine learning with the aim of identifying novel data driven biomarkers which could confirm CM-P and identify dogs at risk of developing SM. The methodology identified four regions of interest as having robust discrimination for CM-P, with 89% sensitivity and 76% specificity. A set of morphological features linked to CM-P were identified. Four regions of interest were also identified as having robust discrimination for SM, with 84% sensitivity and 80% specificity.Overall, these findings shed light on the distinct morphologies related to CM-P and SM, offering the potential for more accurate and objective diagnoses in affected dogs using MRI. These results contribute to the further understanding of the complex pathologies associated with CM and SM in brachycephalic toy pure and mixed breed dogs and support the potential utility of data-driven techniques for advancing our knowledge of these debilitating conditions.

    Keywords: Brachycephaly, canine, image registration, machine learning, Morphologies, MRI

    Received: 06 Sep 2024; Accepted: 23 Dec 2024.

    Copyright: © 2024 Cumber, Scales-Theobald, Rusbridge and WELLS. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Jake Cumber, Centre for Vision, Speech and Signal Processing, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, GU2 7XH., Surrey, United Kingdom

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.