Improving Video‐Based Prediction of Cerebellar Ataxia Severity Using a Pretrained Deep Learning Model
Katsuki Eguchi, Hiroaki Yaguchi, Hisashi Uwatoko, Yuki Iida, Moemi Yamada, Shinsuke Hamada, Kazunori Sato, Sanae Honma, Asako Takei, Atsushi Kawashima, Fumio Moriwaka, Toshiyuki Fukazawa, Ren Togo, Takahiro Ogawa, Miki Haseyama, Kenji Hirata, Kohsuke Kudo, Shinya Tanaka, Ichiro YabeABSTRACT
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.
Methods
Gait videos from patients with DCD were processed using pose estimation, and the extracted time‐series keypoint coordinates were input to a transformer‐based model. Separate models were developed for the SARA total score and the posture and gait subscore (sum of Items 1–3). Models were pretrained on PD gait videos by supervised learning with the Movement Disorder Society–Sponsored Unified Parkinson's Disease Rating Scale (MDS–UPDRS) Part III axial subscore or by self‐supervised masked keypoint reconstruction. Performance was evaluated by leave‐one‐participant‐out cross‐validation (mean absolute error [MAE], coefficient of determination [ R 2 ]), and between‐model differences by participant‐level Wilcoxon signed‐rank tests and bootstrap 95% confidence intervals (CIs).
Results
We analyzed 75 patients with DCD and 141 with PD. For the SARA total score, MAE and R 2 were 1.91 ± 0.06 and 0.78 ± 0.01 with supervised pretraining, 1.95 ± 0.04 and 0.77 ± 0.01 with self‐supervised pretraining, and 2.04 ± 0.07 and 0.74 ± 0.02 with training from scratch; neither strategy significantly reduced participant‐level prediction error ( p = 0.25 and 0.24). For the posture and gait subscore, only supervised pretraining significantly reduced prediction error (mean difference: −0.078; 95% CI: −0.127 to −0.031; p = 0.003).
Conclusions
Pretraining on PD gait data modestly reduced SARA prediction error, reaching statistical significance for the posture and gait subscore with supervised pretraining. Pretraining across neurological disorders may help models acquire transferable representations of pathological gait, but its clinical significance requires validation in larger, multicenter datasets.