Improving Video-Based Prediction of Cerebellar Ataxia Severity Using a Pretrained Deep Learning Model

Background

Ataxia severity in degenerative cerebellar diseases (DCDs) is usually assessed with semi-quantitative clinical rating scales such as the Scale for the Assessment and Rating of Ataxia (SARA), which are subject to variability. Deep learning-based gait analysis offers an objective alternative, but labeled data in DCDs are limited. We investigated whether pretraining on gait videos from patients with Parkinson's disease (PD) could improve SARA score prediction in DCD.