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.