ArticleJournal of veterinary internal medicine2019
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.
Article in Journal of veterinary internal medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Textural analysis and artificial intelligence as decision support tools in the diagnosis of multiple sclerosis - a systematic review.Frontiers in neuroscience · 2024Pooled it
- Artificial Intelligence in Veterinary Neurology: Comparative Insights From Human Medicine and Cross-Species Technology Transfer.Veterinary medicine and science · 2026Review
- Precision Livestock Farming and Biomedical Engineering: Assessing Feed Quality, Animal Health, and Behavior Using Machine Learning for Sensor Data.Sensors (Basel, Switzerland) · 2026Review
- A Veterinary DICOM-Based Deep Learning Denoising Algorithm Can Improve Subjective and Objective Brain MRI Image Quality.Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association · 2025Article
- CT- and MRI-based volumetry for Chiari-like malformation and syringomyelia in Pomeranians.Frontiers in veterinary science · 2025Article
- The effect of MRI-based screening and selection on the prevalence of syringomyelia in the Dutch and Danish Cavalier King Charles Spaniels.Frontiers in veterinary science · 2024Article
- Machine learning algorithms predict canine structural epilepsy with high accuracy.Frontiers in veterinary science · 2024Article
- A data driven approach for soft tissue biomarker identification linked to Chiari-like malformation and syringomyelia.Frontiers in veterinary science · 2024Article
- CT-based and manual external skull measurements for Chiari-like malformation and syringomyelia in Pomeranians.PloS one · 2024Article
- Artificial Intelligence in Veterinary Imaging: An Overview.Veterinary sciences · 2023Review
- Phenotypic characterization of Pomeranians with or without Chiari-like malformation and syringomyelia.Frontiers in veterinary science · 2023Article
- Association between filum terminale internum length and pain in Cavalier King Charles spaniels with and without syringomyelia.Journal of veterinary internal medicine · 2021Article
- Using Deep Learning to Detect Spinal Cord Diseases on Thoracolumbar Magnetic Resonance Images of Dogs.Frontiers in veterinary science · 2021Article
- 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 · 2019Article
- Is foramen magnum decompression for acquired Chiari I malformation like putting a finger in the dyke? - A simplistic overview of artificial intelligence in assessing critical upstream and downstream etiologies.Journal of craniovertebral junction & spineArticle
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundChiari-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/
objectivesTo 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).
methodsRetrospective 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.
resultsCandidate 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.