Assessment of Sensitivity and Specificity of a Machine Learning System for Detection and Monitoring of Mild Cognitive Impairment (Accexible) Through Speech Analysis in a Colombian Population
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 114
- 试验地点
- 1
研究概览
简要总结
Mild Cognitive Impairment (MCI) is frequently underdiagnosed due to its subtle clinical presentation. This study evaluates the diagnostic performance of AcceXible, a speech analysis-based machine learning platform, compared to the Montreal Cognitive Assessment (MoCA) for MCI detection and monitoring in Colombian patients.
A diagnostic test accuracy study will be conducted within a primary care setting (EPS Sanitas), including prior validation of the AcceXible protocol in the Colombian healthcare context.
The study pursues two primary aims: (1) to validate the AcceXible tool in a Colombian population, and (2) to demonstrate that AcceXible achieves high diagnostic accuracy for early MCI detection and longitudinal monitoring relative to the MoCA.
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
- 60 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Ability to independently operate a mobile or portable device with internet connectivity and integrated microphone
- •Provision of voluntary written informed consent prior to study participation
排除标准
- •Diagnosed psychiatric disorder or cognitive impairment not attributable to neurodegenerative etiology
- •Visual impairment sufficient to preclude reading on-screen text or perceiving visual stimuli
- •Illiteracy
- •Hearing impairment sufficient to preclude comprehension of verbal instructions or detection of auditory test-onset cues