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, ISSN: 1939-1676, Vol: 33, Issue: 6, Page: 2665-2674
2019
- 16Citations
- 113Captures
- 2Mentions
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Metrics Details
- Citations16
- Citation Indexes16
- 16
- CrossRef9
- Captures113
- Readers113
- 113
- Mentions2
- News Mentions2
- News2
Most Recent News
AI Could Help Diagnose Dogs Suffering From Chronic Pain, Identify Facial Changes Associated With Chiari-Like Malformation: University of Surrey
[TNSbiologyresearch-Journal of Veterinary Internal Medicine] -- The University of Surrey issued the following news release: A new artificial intelligence (AI) technique developed by the University
Article 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.
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