Gait Video-Based Prediction of Severity of Cerebellar Ataxia Using Deep Neural Networks.

BACKGROUND

Pose estimation algorithms applied to two-dimensional videos evaluate gait disturbances; however, a few studies have used this method to evaluate ataxic gait.

OBJECTIVE

The aim was to assess whether a pose estimation algorithm can predict the severity of cerebellar ataxia by applying it to gait videos.

METHODS

We video-recorded 66 patients with degenerative cerebellar diseases performing the timed up-and-go test. Key points from the gait videos extracted by a pose estimation algorithm were input into a deep learning model to predict the Scale for the Assessment and Rating of Ataxia (SARA) score. We also evaluated video segments that the model focused on to predict ataxia severity.

RESULTS

The model achieved a root-mean-square error of 2.30 and a coefficient of determination of 0.79 in predicting the SARA score. It primarily focused on standing, turning, and body sway to assess severity.

CONCLUSIONS

This study demonstrated that the model may capture gait characteristics from key-point data and has the potential to predict SARA scores. © 2025 International Parkinson and Movement Disorder Society.