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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
BACKGROUND: Chiari‐like malformation (CM) is a complex malformation of the skull and cranial cervical vertebrae that potentially results in pain and secondary syringomyelia (SM). Chiari‐like malformation‐associated pain (CM‐P) can be challenging to diagnose. We propose a machine learning approach to...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley & Sons, Inc.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6872629/ https://www.ncbi.nlm.nih.gov/pubmed/31552689 http://dx.doi.org/10.1111/jvim.15621 |
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author | Spiteri, Michaela Knowler, Susan P. Rusbridge, Clare Wells, Kevin |
author_facet | Spiteri, Michaela Knowler, Susan P. Rusbridge, Clare Wells, Kevin |
author_sort | Spiteri, Michaela |
collection | PubMed |
description | BACKGROUND: Chiari‐like malformation (CM) is a complex malformation of the skull and cranial cervical vertebrae that potentially results in pain and secondary syringomyelia (SM). Chiari‐like malformation‐associated pain (CM‐P) can be challenging to diagnose. We propose a machine learning approach to characterize morphological changes in dogs that may or 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: To understand neuromorphological change and to identify image‐based biomarkers in dogs with CM‐P and symptomatic SM (SM‐S) using a novel machine learning approach, with the aim of increasing the understanding of these disorders. ANIMALS: Thirty‐two client‐owned Cavalier King Charles Spaniels (CKCSs; 11 controls, 10 CM‐P, 11 SM‐S). METHODS: Retrospective study using T2‐weighted midsagittal Digital Imaging and Communications in Medicine (DICOM) anonymized images, which then were mapped to images of an 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 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‐S biomarkers, collectively. CONCLUSIONS AND CLINICAL IMPORTANCE: Machine learning techniques can assist CM/SM diagnosis and facilitate understanding of abnormal morphology location with the potential to be applied to a variety of breeds and conformational diseases. |
format | Online Article Text |
id | pubmed-6872629 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | John Wiley & Sons, Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-68726292019-11-25 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, Michaela Knowler, Susan P. Rusbridge, Clare Wells, Kevin J Vet Intern Med SMALL ANIMAL BACKGROUND: Chiari‐like malformation (CM) is a complex malformation of the skull and cranial cervical vertebrae that potentially results in pain and secondary syringomyelia (SM). Chiari‐like malformation‐associated pain (CM‐P) can be challenging to diagnose. We propose a machine learning approach to characterize morphological changes in dogs that may or 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: To understand neuromorphological change and to identify image‐based biomarkers in dogs with CM‐P and symptomatic SM (SM‐S) using a novel machine learning approach, with the aim of increasing the understanding of these disorders. ANIMALS: Thirty‐two client‐owned Cavalier King Charles Spaniels (CKCSs; 11 controls, 10 CM‐P, 11 SM‐S). METHODS: Retrospective study using T2‐weighted midsagittal Digital Imaging and Communications in Medicine (DICOM) anonymized images, which then were mapped to images of an 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 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‐S biomarkers, collectively. CONCLUSIONS AND CLINICAL IMPORTANCE: Machine learning techniques can assist CM/SM diagnosis and facilitate understanding of abnormal morphology location with the potential to be applied to a variety of breeds and conformational diseases. John Wiley & Sons, Inc. 2019-09-24 2019 /pmc/articles/PMC6872629/ /pubmed/31552689 http://dx.doi.org/10.1111/jvim.15621 Text en © 2019 The Authors. Journal of Veterinary Internal Medicine published by Wiley Periodicals, Inc. on behalf of the American College of Veterinary Internal Medicine. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | SMALL ANIMAL Spiteri, Michaela Knowler, Susan P. Rusbridge, Clare Wells, Kevin 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 |
title | 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 |
title_full | 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 |
title_fullStr | 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 |
title_full_unstemmed | 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 |
title_short | 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 |
title_sort | 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 |
topic | SMALL ANIMAL |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6872629/ https://www.ncbi.nlm.nih.gov/pubmed/31552689 http://dx.doi.org/10.1111/jvim.15621 |
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