Objective
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.
Conclusion
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.
