帕金森病中运动和认知症状的可访问性评估:整合大型数据集,机器学习和视频会议
Avigail Lithwick Algon1,2, William Saban3,4,5
1Center for Accessible Neuropsychology, Tel Aviv, Israel.
NPJ digital medicine
|February 7, 2026
概括
使用视频会议进行远程帕金森病 (PD) 评估的准确性很高. 运动和认知视频会议 (MaC-VC) 协议使得PD评估具有可扩展性,可访问性和地理多样性.
科学领域:
- 神经学 神经学
- 数字健康数字健康
- 机器学习 机器学习
背景情况:
- 对帕金森病 (PD) 的个人评估在可访问性,可扩展性和地理代表性方面存在重大挑战.
- 需要远程评估工具来克服这些局限性,并改善患者获得护理的机会.
研究的目的:
- 开发和验证一个远程评估协议,运动和认知视频会议 (MaC-VC),用于帕金森病.
- 通过比较其性能与面对面的专家评估来评估Mac-VC的可行性和通用性.
主要方法:
- 开发了Mac-VC协议,用于非专家远程管理MDS-UPDRS III和MoCA测试.
- 使用MaC-VC.收集了来自不同地理位置的145名参与者的数据.
- 将MaC-VC结果与来自帕金森氏症进展标志物倡议 (PPMI) 研究的专家评级,面对面评估的大量数据集 (n=1264) 相比较.
- 利用机器学习 (ML) 进行双向交叉数据集验证.
主要成果:
- 一个简短的,在线可行的MDS-UPDRS III分数解释了MDS-UPDRS III分数中95%的差异.
- 在MDS-UPDRS-III,MoCA,疾病持续时间和性别的在线和面对面数据集中,在早期和晚期PD组之间观察到一致的显著趋势.
- 在数据集内部和跨数据集验证中,ML分类器表现出高性能 (AUC>0.9),表明了跨不同人群的预测能力.
结论:
- MaC-VC协议是一个可行的和可通用的工具,用于远程评估帕金森病.
- MaC-VC提高了PD评估的可访问性,可扩展性和地理多样性.
- 这种方法支持更广泛,更公平地参与PD研究和临床监测.
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