Computer vision uncovers three fundamental dimensions of levodopa-responsive motor improvement in Parkinson’s disease

Computer vision uncovers three fundamental dimensions of levodopa-responsive motor improvement in Parkinson’s disease

Abstract We developed VisionMD, an AI computer vision platform, analyzing over 1200 clinical videos of Parkinson’s patients’ hand movements across 13 years. This large-scale, markerless analysis identified three kinematic domains (speed, consistency, timing/scale) reliably improved by levodopa. Our...

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Bibliographic Details
Main Authors: Florian Lange, Diego L. Guarin, Esther Ademola, Dalia Mahdy, Gabriela Acevedo, Thorsten Odorfer, Joshua K. Wong, Jens Volkmann, Robert Peach, Martin Reich
Format: Article
Language:English
Published: Nature Portfolio 2025-05-01
Series:npj Parkinson's Disease
Online Access:https://doi.org/10.1038/s41531-025-00999-w
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Summary:Abstract We developed VisionMD, an AI computer vision platform, analyzing over 1200 clinical videos of Parkinson’s patients’ hand movements across 13 years. This large-scale, markerless analysis identified three kinematic domains (speed, consistency, timing/scale) reliably improved by levodopa. Our method offers objective, quantitative motor assessment, reducing subjectivity and enhancing reproducibility compared to traditional scales.
ISSN:2373-8057

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