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

摘要

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.