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智能手机测试可以量化多发性硬化症下肢功能障碍.

Kimberly Jin1, Peter Kosa1, Bibiana Bielekova1

  • 1Laboratory of Clinical Immunology and Microbiology, Neuroimmunological Diseases Section, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, United States.

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|December 2, 2024
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概括
此摘要是机器生成的。

智能手机测试可以可靠地测量神经疾病患者的下肢运动功能. 这项技术有助于远程监测和评估患者,改善慢性疾病的护理.

关键词:
脚敲打,就是脚的敲打.步态分析 步态分析运动器功能 运动器功能多发性硬化症多发性硬化症神经学检查 神经学检查神经学 神经学 神经学智能手机应用程序 智能手机应用程序远程医疗服务是远程医疗服务.

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科学领域:

  • 神经学 神经学
  • 生物医学工程 生物医学工程
  • 数字健康数字健康

背景情况:

  • 越来越多的神经科医生短缺和老龄化人口压力慢性神经疾病的护理.
  • 远程医疗具有潜力,但在监测神经障碍方面面临挑战.
  • 智能手机技术为远程,自主管理的神经评估提供了机会.

研究的目的:

  • 评估神经功能测试套件 (NeuFun-TS) 的智能手机测试,以量化下肢运动功能.
  • 评估NeuFun-TS数字生物标志物与多发性硬化症 (MS) 患者的传统神经学评估和MRI结果的相关性.

主要方法:

  • 108名多发性硬化症患者接受了神经学检查,脑部成像,并完成了NeuFun-TS智能手机测试.
  • 来自NeuFun-TS的数字生物标志物与残疾量表和MRI发现相关.
  • 机器学习模型被开发用于使用数字生物标志物预测身体残疾.

主要成果:

  • NeuFun-TS生物标志物与步态和下肢功能结果有很强的相关性 (斯皮尔曼 ρ > 0.8).
  • 与全球残疾指标 (例如,EDSS, ρ 0.46-0.65) 和成像结果 (例如,髓缩, ρ ~ 0.4) 发现了显著的相关性.
  • 结合可靠的数字特征的机器学习模型在预测身体残疾方面超过了个别生物标志物.

结论:

  • NeuFun-TS测试产生了可靠的数字生物标志物,用于下肢运动功能.
  • 基于智能手机的神经评估为远程监测和残疾量化提供了可行的工具.