视频分析揭示了快速眼动睡眠行为障碍和帕金森病中运动迟缓的早期迹象。

Video analysis reveals early signs of Bradykinesia in REM sleep behavior disorder and Parkinson's disease.

作者信息

Guarín Diego L, Acevedo Gabriela, Calonge Carolina, Wong Joshua K, McFarland Nikolaus R, Ramirez-Zamora Adolfo, Vaillancourt David E

机构信息

Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, USA.

Fixel Institute for Neurological Disease, College of Medicine, University of Florida, Gainesville, FL, USA.

出版信息

NPJ Parkinsons Dis. 2025 Jul 30;11(1):222. doi: 10.1038/s41531-025-01082-0.

DOI:10.1038/s41531-025-01082-0

PMID:40739299

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12311115/

Abstract

Idiopathic REM sleep behavior disorder (iRBD) is a strong predictor of neurodegenerative diseases like Parkinson's disease (PD). Early detection of motor impairments such as bradykinesia is critical for identifying at-risk populations. This study analyzed Finger Tapping Task videos from 66 participants, including healthy controls (HC) and individuals with iRBD and PD. Only videos that received a clinician score of zero on the MDS-UPDRS Part-III finger tapping item were analyzed. Movement amplitude, speed, and their decrements during the task were directly estimated from the videos using machine learning algorithms. Bradykinesia and hypokinesia were detectable in PD but not in iRBD, while decrement in movement amplitude and speed were observed in PD and iRBD. Classification models achieved 81.5% accuracy distinguishing PD from HC, 79.8% distinguishing iRBD from HC, and 81.7% differentiating iRBD from PD. Video-based assessments offer a low-cost, scalable solution for supporting the identification individuals at risk of developing neurodegenerative diseases.

摘要

特发性快速眼动睡眠行为障碍（iRBD）是帕金森病（PD）等神经退行性疾病的有力预测指标。早期发现运动障碍，如运动迟缓，对于识别高危人群至关重要。本研究分析了66名参与者的手指敲击任务视频，包括健康对照者（HC）以及患有iRBD和PD的个体。仅分析了在MDS-UPDRS第三部分手指敲击项目中临床医生评分为零的视频。使用机器学习算法直接从视频中估计任务期间的运动幅度、速度及其衰减情况。运动迟缓在PD中可检测到，但在iRBD中未检测到，而在PD和iRBD中均观察到运动幅度和速度的衰减。分类模型区分PD与HC的准确率达到81.5%，区分iRBD与HC的准确率达到79.8%，区分iRBD与PD的准确率达到81.7%。基于视频的评估为支持识别有神经退行性疾病风险的个体提供了一种低成本、可扩展的解决方案。