Early Detection of Alzheimer's Disease and Affective Disorders by Automated Voice and Speech Analysis
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 100
- 试验地点
- 1
- 主要终点
- Build and validate speech-based machine learning models for relevant Phenotype detection through access to phenotyped patients from reference memory center.
研究概览
简要总结
PLATA aims to develop an algorithm to identify vocal biomarkers of Alzheimer's dementia.
Using data collected as part of routine care, speech patterns will be compared to known biomarkers of Alzheimer's disease, such as amyloid 1-42 and p-Tau in CSF (cerebrospinal fluid).
If biomarkers of speech can be identified in Alzheimer's disease, it is possible that patients and research participants will no longer need to undergo need to undergo the intensive and invasive baseline biomarker methods currently used, such as lumbar punctures and PET scans.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 50 Years 至 100 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Age ≥ 50 years
- •Diagnosis relevant biomarker and neuropsychological data already available
- •Cognitively healthy to very mild dementia (CDR score max. 0.5)
- •Sufficient knowledge of the study language to understand study information, non opposition form,and questionnaires
- •Expression of non opposition
排除标准
- •Hearing problems
- •Patient protected by law, under guardianship or curator ship, or not able to participate in a clinical study according to the article L.1121-16 of the French Public Health Code
结局指标
主要结局
Build and validate speech-based machine learning models for relevant Phenotype detection through access to phenotyped patients from reference memory center.
时间窗: 20 minutes
Speech biomarker algorithm(s)
次要结局
未报告次要终点