Brain and behavior · 2026
Improving Video-Based Prediction of Cerebellar Ataxia Severity Using a Pretrained Deep Learning Model.
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Eguchi Katsuki, Yaguchi Hiroaki, Uwatoko Hisashi, Iida Yuki, Yamada Moemi, Hamada Shinsuke, Sato Kazunori, Honma Sanae, Takei Asako, Kawashima Atsushi, Moriwaka Fumio, Fukazawa Toshiyuki, Togo Ren, Ogawa Takahiro, Haseyama Miki, Hirata Kenji, Kudo Kohsuke, Tanaka Shinya, Yabe Ichiro
Аннотация
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 [R2]), 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 R2 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.
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 [R2]), 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 R2 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.