计算机视觉揭示了帕金森病中左旋多巴反应性运动改善的三个基本维度。
Computer vision uncovers three fundamental dimensions of levodopa-responsive motor improvement in Parkinson's disease.
作者信息
Lange Florian, Guarin Diego L, Ademola Esther, Mahdy Dalia, Acevedo Gabriela, Odorfer Thorsten, Wong Joshua K, Volkmann Jens, Peach Robert, Reich Martin
机构信息
Department of Neurology, University of Würzburg, Würzburg, Germany.
Movement Estimation and Analysis Laboratory, Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, USA.
出版信息
NPJ Parkinsons Dis. 2025 May 28;11(1):140. doi: 10.1038/s41531-025-00999-w.
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.
我们开发了VisionMD，这是一个人工智能计算机视觉平台，分析了13年间超过1200段帕金森病患者手部运动的临床视频。这种大规模的、无标记分析确定了左旋多巴能可靠改善的三个运动学领域（速度、一致性、时间/尺度）。与传统量表相比，我们的方法提供了客观、定量的运动评估，减少了主观性并提高了可重复性。
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