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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.

DOI:10.1038/s41531-025-00999-w
PMID:40436873
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12119790/
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.

摘要

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。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ceca/12119790/49f0f8e912d6/41531_2025_999_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ceca/12119790/49f0f8e912d6/41531_2025_999_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ceca/12119790/49f0f8e912d6/41531_2025_999_Fig1_HTML.jpg

相似文献

1
Computer vision uncovers three fundamental dimensions of levodopa-responsive motor improvement in Parkinson's disease.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。
NPJ Parkinsons Dis. 2025 May 28;11(1):140. doi: 10.1038/s41531-025-00999-w.
2
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J Parkinsons Dis. 2025 Mar;15(2):349-360. doi: 10.1177/1877718X241312605. Epub 2025 Feb 13.
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Automatic extraction of upper-limb kinematic activity using deep learning-based markerless tracking during deep brain stimulation implantation for Parkinson's disease: A proof of concept study.基于深度学习的无标记跟踪技术在帕金森病脑深部刺激植入术中上肢运动学活动的自动提取:概念验证研究。
PLoS One. 2022 Oct 20;17(10):e0275490. doi: 10.1371/journal.pone.0275490. eCollection 2022.
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Vision-based assessment of parkinsonism and levodopa-induced dyskinesia with pose estimation.基于视觉的帕金森病和左旋多巴诱导运动障碍的姿势估计评估。
J Neuroeng Rehabil. 2018 Nov 6;15(1):97. doi: 10.1186/s12984-018-0446-z.
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本文引用的文献

1
VisionMD: an open-source tool for video-based analysis of motor function in movement disorders.VisionMD:一种用于运动障碍中基于视频的运动功能分析的开源工具。
NPJ Parkinsons Dis. 2025 Feb 4;11(1):27. doi: 10.1038/s41531-025-00876-6.
2
What the trained eye cannot see: Quantitative kinematics and machine learning detect movement deficits in early-stage Parkinson's disease from videos.训练有素的眼睛无法察觉:定量运动学和机器学习可从视频中检测早期帕金森病的运动缺陷。
Parkinsonism Relat Disord. 2024 Oct;127:107104. doi: 10.1016/j.parkreldis.2024.107104. Epub 2024 Aug 14.
3
FastEval Parkinsonism: an instant deep learning-assisted video-based online system for Parkinsonian motor symptom evaluation.
快速评估帕金森病:一种即时的基于深度学习辅助视频的帕金森运动症状在线评估系统。
NPJ Digit Med. 2024 Feb 8;7(1):31. doi: 10.1038/s41746-024-01022-x.
4
Clinically Informed Automated Assessment of Finger Tapping Videos in Parkinson's Disease.临床知情的帕金森病手指敲击视频自动评估。
Sensors (Basel). 2023 Nov 13;23(22):9149. doi: 10.3390/s23229149.
5
Parkinsonian Hand or Clinician's Eye? Finger Tap Bradykinesia Interrater Reliability for 21 Movement Disorder Experts.帕金森手还是临床医生的眼?21 位运动障碍专家手指叩击运动迟缓的组内信度。
J Parkinsons Dis. 2023;13(4):525-536. doi: 10.3233/JPD-223256.
6
An Evaluation of KELVIN, an Artificial Intelligence Platform, as an Objective Assessment of the MDS UPDRS Part III.KEILIN 人工智能平台对 MDS UPDRS 第三部分的客观评估
J Parkinsons Dis. 2022;12(7):2223-2233. doi: 10.3233/JPD-223493.
7
Video-Based Analyses of Parkinson's Disease Severity: A Brief Review.基于视频的帕金森病严重程度分析:简要综述。
J Parkinsons Dis. 2021;11(s1):S83-S93. doi: 10.3233/JPD-202402.
8
Evolving concepts on bradykinesia.关于运动徐缓的概念演变。
Brain. 2020 Mar 1;143(3):727-750. doi: 10.1093/brain/awz344.
9
Measuring Parkinson's disease over time: The real-world within-subject reliability of the MDS-UPDRS.随着时间推移测量帕金森病:MDS-UPDRS 的真实世界内个体可靠性。
Mov Disord. 2019 Oct;34(10):1480-1487. doi: 10.1002/mds.27790. Epub 2019 Jul 10.
10
Does the MDS-UPDRS provide the precision to assess progression in early Parkinson's disease? Learnings from the Parkinson's progression marker initiative cohort.MDS-UPDRS 是否能精确评估早期帕金森病的进展?帕金森病进展标志物倡议队列的研究结果。
J Neurol. 2019 Aug;266(8):1927-1936. doi: 10.1007/s00415-019-09348-3. Epub 2019 May 9.