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Updated: Feb 25, 2026

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Oral Health Assessment by Lay Personnel for Older Adults
Published on: February 2, 2020
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声乐生物标志物用于老年人的健康评估:一个范围审查协议
Mehrdad Amir-Behghadami1,2,3, Kamal Gholipour4,2, Zeinab Mohammadzadeh1,3
1Student Research Committee (SRC), Tabriz University of Medical Sciences, Tabriz, Iran z.mohammadzadeh20@gmail.com behghadami.m@gmail.com.
BMJ open
|February 23, 2026
概括
本范围审查综合了对人工智能驱动的音频生物标志物用于检测老年人神经疾病的研究. 它旨在巩固关于使用语音分析用于早期诊断和管理痴呆症等疾病的碎片化知识.
科学领域:
- 神经学 神经学
- 生物标志物 生物标志物
- 人工智能的人工智能
背景情况:
- 人口老龄化需要可访问的,非侵入性的工具来检测神经疾病.
- 目前神经系统疾病的诊断方法往往是侵入性的,昂贵的和不常见的.
- 人类的声音显示出作为一个数字生物标志物的潜力,反映了生理和认知变化.
研究的目的:
- 系统地对人工智能驱动的音频生物标志物的研究进行整理和综合,用于神经疾病的检测和管理.
- 解决当前关于神经疾病声部分析的文献碎片化的问题.
- 专注于老年人 (65岁以上) 患有诸如认知衰退,痴呆症,帕金森病,阿尔茨海默病和抑郁症等疾病.
主要方法:
- 根据PRISMA-ScR指南和Arksey和O'Malley的框架进行的范围审查.
- 使用人口,概念,上下文 (PCC) 框架定义的资格标准.
- 在多个数据库 (PubMed,Scopus,Web of Science等) 中进行全面的文献搜索. 对于波斯语和英语文章,从2012年到2026年.
- 数据由两个独立的审稿人提取和综合,其中的差异由第三名审稿人解决.
主要成果:
- 审查协议概述了一种系统的方法来综合现有研究.
- 数据将以汇总表,图表和图形的形式呈现.
- 该综合旨在提供人工智能驱动的音频生物标志物在神经疾病管理中的全面概述.
结论:
- 这一范围审查将巩固有关老年人神经疾病的AI驱动音频生物标志物的碎片化知识.
- 研究结果将为研究人员,临床医生和决策者提供宝贵的见解.
- 该研究强调了语音分析作为早期检测和监测的非侵入性工具的潜力.
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