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

Spiteri, M., Knowler, S.P., Rusbridge, C. and 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.

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Abstract

Background: Chiari-like malformation (CM) is a complex malformation of the skull and cranial cervical vertebrae potentially resulting in pain and secondary syringomyelia (SM). CM associated pain can be challenging to diagnose [35]. We propose a machine learning approach to characterize morphological changes in dogs that may/may not be apparent to human observers. This data driven approach can remove potential bias (or blindness) that may be produced by a hypothesis driven expert observer approach.

Hypothesis/Objectives: Using a novel machine learning approach to understand neuromorphological change and to identify image-based biomarkers in dogs with CM associated pain (CM-P) and symptomatic SM (SM-S), with the aim of deepening the understanding on these disorders. Animals: 32 client owned Cavalier King Charles Spaniels (CKCS) (11 controls, 10 CM-P, 11 SM-S)

Methods: Retrospective study using T2W midsagittal DICOM anonymized images which were mapped to a images of a average clinically normal CKCS reference using Demons image registration. Key deformation features were automatically selected from the resulting deformation maps. A kernelized Support Vector Machine was used for classifying characteristic localized changes in morphology.

Results: Candidate biomarkers were identified with receiver operating characteristic (ROC) curves with area under the curve (AUC) of 0.78 (sensitivity = 82%; specificity = 69%) for the CM-P biomarkers collectively, and an AUC of 0.82 (sensitivity = 93%; specificity = 67%) for the SM biomarkers collectively.

Conclusions and clinical importance: Machine learning techniques can assist CM/SM diagnosis and understand abnormal morphology location with the potential to be applied to a variety of breeds and conformational diseases.

Item Type:	Article
Divisions :	Faculty of Health and Medical Sciences > School of Veterinary Medicine Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :	Name Email ORCID Spiteri, M. michaela.spiteri@surrey.ac.uk Knowler, S.P. s.knowler@surrey.ac.uk Rusbridge, C. c.rusbridge@surrey.ac.uk Wells, K. K.Wells@surrey.ac.uk
Date :	2019
Funders :	Petplan Charitable Trust, Cavalier Matters Charity
Grant Title :	Pump Primer Scientific Grant
Copyright Disclaimer :	Copyright © 2019 The Authors. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
Uncontrolled Keywords :	MRI; Cavalier King Charles Spaniel; Brachycephaly; Biomarker; Craniosynostosis; Machine Learning; Image Registration
Related URLs :	Publisher
Depositing User :	Clive Harris
Date Deposited :	10 Sep 2019 13:23
Last Modified :	16 Sep 2019 13:15
URI:	http://epubs.surrey.ac.uk/id/eprint/852598

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