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Gait video-based prediction of unified Parkinson’s disease rating scale score: a retrospective study by Katsuki Eguchi; Ichigaku Takigawa; Shinichi Shirai; Ikuko Takahashi-Iwata; Masaaki Matsushima; Takahiro Kano; Hiroaki Yaguchi; Ichiro Yabe is a Medicine article available to read on EtoBox.

What is Gait video-based prediction of unified Parkinson’s disease rating scale score: a retrospective study about?

## Background The diagnosis of Parkinson’s disease (PD) and evaluation of its symptoms require in-person clinical examination. Remote evaluation of PD symptoms is desirable, especially during a pandemic such as the coronavirus disease 2019 pandemic. One potential method to remotely evaluate PD motor impairments is video-based analysis. In this study, we aimed to assess the feasibility of predicting the Unified Parkinson’s Disease Rating Scale (UPDRS) score from gait videos using a convolutional neural network (CNN) model. ## Methods We retrospectively obtained 737 consecutive gait videos of 74 patients with PD and their corresponding neurologist-rated UPDRS scores. We utilized a CNN model for predicting the total UPDRS part III score and four subscores of axial symptoms (items 27, 28, 29, and 30), bradykinesia (items 23, 24, 25, 26, and 31), rigidity (item 22) and tremor (items 20 and 21). We trained the model on 80% of the gait videos and used 10% of the videos as a validation dataset. We evaluated the predictive performance of the trained model by comparing the model-predicted score with the neurologist-rated score for the remaining 10% of videos (test dataset). We calculated the

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Author

Katsuki Eguchi; Ichigaku Takigawa; Shinichi Shirai; Ikuko Takahashi-Iwata; Masaaki Matsushima; Takahiro Kano; Hiroaki Yaguchi; Ichiro Yabe

Publisher

Springer Science and Business Media LLC

Published

2023

Field

Medicine (Health Sciences)