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 datadriven 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.Abbreviations: AUC, area under the curve; CKCS, Cavalier King Charles Spaniel; CM, Chiari-like malformation; CM-N, control dogs: no SM no CM pain; 4 years of age and older; CM-P, pain associated with Chiari-like malformation; CSF, cerebrospinal fluid; DICOM, Digital Imaging and Communications in Medicine; FPR, false-positive rate (also known as 1 − specificity); ICC, intraclass correlation coefficient; MRI, magnetic resonance imaging; PCA, principal component analysis; ROC, receiver operating characteristic; SFFS, sequential floating forward selection; SM, syringomyelia; SM-S, syringomyelia and associated clinical signs; SVM, support vector machine; TPR, true-positive rate (also known as sensitivity).