Feasibility of differentiating gait in Parkinson's disease and spinocerebellar degeneration using a pose estimation algorithm in two-dimensional video

Eguchi, Katsuki; Yaguchi, Hiroaki; Uwatoko, Hisashi; Iida, Yuki; Hamada, Shinsuke; Honma, Sanae; Takei, Asako; Moriwaka, Fumio; Yabe, Ichiro

Feasibility of differentiating gait in Parkinson's disease and spinocerebellar degeneration using a pose estimation algorithm in two-dimensional video

Eguchi, Katsuki; Yaguchi, Hiroaki; Uwatoko, Hisashi et al.

Journal of the neurological sciences, 2024, 464, 123158

Permalink : https://hdl.handle.net/2115/96112

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論文情報

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アクセス権	open access
URI	https://hdl.handle.net/2115/96112
タイトル	Feasibility of differentiating gait in Parkinson's disease and spinocerebellar degeneration using a pose estimation algorithm in two-dimensional video
著者	Eguchi, Katsuki ; Yaguchi, Hiroaki ; Uwatoko, Hisashi ; Iida, Yuki ; Hamada, Shinsuke ; Honma, Sanae ; Takei, Asako ; Moriwaka, Fumio ; Yabe, Ichiro
e-Rad_Researcher 20852635 著者名 Eguchi, Katsuki
e-Rad_Researcher 00421975 著者名 Yaguchi, Hiroaki
著者名 Uwatoko, Hisashi
著者名 Iida, Yuki
著者名 Hamada, Shinsuke
著者名 Honma, Sanae
著者名 Takei, Asako
著者名 Moriwaka, Fumio
e-Rad_Researcher 60372273 著者名 Yabe, Ichiro
言語	英語
キーワード	Parkinson's disease
	Spinocerebellar degeneration
	Ataxia
	Gait disturbance
	Machine learning
発行日	2024-09-15
出版者	Elsevier
収録物名	Journal of the neurological sciences
巻(号)	464
ページ	123158
抄録	Background Although pose estimation algorithms have been used to analyze videos of patients with Parkinson's disease (PD) to assess symptoms, their feasibility for differentiating PD from other neurological disorders that cause gait disturbances has not been evaluated yet. We aimed to determine whether it was possible to differentiate between PD and spinocerebellar degeneration (SCD) by analyzing video recordings of patient gait using a pose estimation algorithm. Methods We videotaped 82 patients with PD and 61 patients with SCD performing the timed up-and-go test. A pose estimation algorithm was used to extract the coordinates of 25 key points of the participants from these videos. A transformer-based deep neural network (DNN) model was trained to predict PD or SCD using the extracted coordinate data. We employed a leave-one-participant-out cross-validation method to evaluate the predictive performance of the trained model using accuracy, sensitivity, and specificity. As there were significant differences in age, weight, and body mass index between the PD and SCD groups, propensity score matching was used to perform the same experiment in a population that did not differ in these clinical characteristics. Results The accuracy, sensitivity, and specificity of the trained model were 0.86, 0.94, and 0.75 for all participants and 0.83, 0.88, and 0.78 for the participants extracted by propensity score matching. Conclusion The differentiation of PD and SCD using key point coordinates extracted from gait videos and the DNN model was feasible and could be used as a collaborative tool in clinical practice and telemedicine.
資源タイプ	学術雑誌論文
出版タイプ	Accepted Manuscript
権利情報	Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
権利情報	© 2024, Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
PISSN	0022-510X
EISSN	1878-5883
関連情報 (isVersionOf)	出版社版DOI https://doi.org/10.1016/j.jns.2024.123158