Journal Article

DZNE-2026-00733

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Frequency-based deep learning to identify subtle postural instability in early, untreated Parkinson's disease.

Engel, D. (First author)DZNE* ; Burgos, P. ; Carlson-Kuhta, P. ; Talman, L. ; Quinn, J. F. ; Vinehout, K. ; Greulich, R. S. ; Horak, F. B. ; Mancini, M.

2026
Springer Nature [London]

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Please use a persistent id in citations: doi:10.1038/s41531-026-01365-0

Abstract: Despite evidence of early neurodegeneration, postural instability is commonly associated with later stages of Parkinson's disease (PD), mainly due to a lack of sensitive measures. Here, we aim to provide a sensitive, easily obtainable objective measure of postural instability for earlier clinical detection. We assessed postural sway in 40 newly diagnosed, untreated individuals with PD and 79 age-matched healthy controls while they stood quietly for 30 seconds with their eyes open and feet together. Body sway was recorded with a single accelerometer placed at the lumbar spine. We trained a convolutional neural network (CNN) to distinguish between the groups based on the frequency information of their sway signals. Our models reached an average accuracy, sensitivity, and specificity of 98.9%, 97.7%, and 98.9%, respectively. This suggests that characteristic frequency features of postural sway reflect subtle postural impairments in early PD, with great potential to translate into clinical applications.

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Contributing Institute(s):

1. Bonn common (Bonn common)

Research Program(s):

1. 899 - ohne Topic (POF4-899) (POF4-899)

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