生物标志物 生物标志物
Yuting Song1, Bingjie Wu1, Menghan Zhou1
1Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 26, 2025
概括
这项研究表明,人工智能 (AI) 可以通过分析面部和生理反应来检测行为障碍. 人工智能衍生的心率 (HR) 与老年人的抑郁症得分有很强的相关性,有助于早期检测.
科学领域:
- 老年医学 老年医学
- 人工智能的人工智能
- 行为科学 行为科学
背景情况:
- 早期发现行为障碍对于及时干预至关重要.
- 传统的评估工具有其局限性,例如实践效应和人员短缺.
- 行为症状与生理变化和面部表情有关.
研究的目的:
- 开发一种基于人工智能的新系统,用于分析面部和生理反应.
- 为了促进在风险社区个人的行为障碍的早期检测.
- 评估AI衍生的心率 (HR) 和老年抑郁量表-15 (GDS-15) 分数之间的相关性.
主要方法:
- 从社区认知查计划中招募了66名老年人 (60岁以上).
- 在休息和GDS-15评估期间收集了面部视频样本.
- 开发了人工智能算法,从面部视频中提取HR,并将其与GDS-15分数相关联.
主要成果:
- 基于AI的HR检测实现了平均平均误差为10.1 (休息) 和13.9 (评估) bpm.
- 在抑郁症组 (GDS-15 ≥5) 中,HR与GDS-15得分 (休息时间为0.87,评估期间为0.98) 的相关性很高.
- 在正常组 (GDS-15 0-4),HR和GDS-15之间的相关性是可以忽略不计的 (0.13休息,0.06在评估期间).
结论:
- 初步发现支持AI衍生的HR从面部分析作为行为障碍查的有希望的工具.
- 在老年人中,较高的抑郁症状与较高的HR有关.
- 未来的工作将整合面部特征和生理措施,以提高诊断准确度.
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