Artificial Intelligence AI Driven Analysis of Electroencephalography for the Detection of Dementia
试验速览
- 阶段
- 2/3 期
- 状态
- 尚未招募
- 发起方
- 入组人数
- 60
- 试验地点
- 1
- 主要终点
- To evaluate the diagnostic accuracy of the NEMA AI EEG software in identifying patients with dementia as compared to the standard clinical and neuropsychological assessment methods. The analysis will focus on the software’s ability to accurately differentiate between dementia and non-dementia cases using brainwave-derived cognitive biomarkers.
研究概览
简要总结
The increasing global prevalence of dementia, coupled with the limitations of conventional diagnostic methods, highlights the need for non-invasive, scalable, and cost-effective solutions. Electroencephalography (EEG)-based cognitive analytics powered by AI presents an opportunity to bridge this gap by detecting subtle electrophysiological changes before clinical symptoms manifest. EEG is a widely available and non-invasive tool, which can be harnessed for this purpose. Traditional dementia diagnostics rely on neuropsychological assessment, MRI scans, or PET imaging, which are costly, time-consuming, and inaccessible in many regions. There is a critical need for an affordable and scalable EEG-based screening tool to enable early diagnosis, intervention and improve patient outcomes. Develop an AI-driven EEG analysis framework to detect neuro cognitive impairment (dementia) compared to healthy controls
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 盲法
- None
入排标准
- 年龄范围
- 18.00 Year(s) 至 75.00 Year(s)（—）
- 性别
- All
入选标准
- •Cognitive deficits interfering with independence in everyday activities in case of major neurocognitive disorder or not interfering with independence in everyday activities in the case of minor neurocognitive disorder.
排除标准
- •Patients with other causes of neurocognitive impairment like metabolic, head injury, seizures, delirium etc.
- •Patients with neurocognitive impairment due to psychiatric disorders like depression, schizophrenia etc.
结局指标
主要结局
To evaluate the diagnostic accuracy of the NEMA AI EEG software in identifying patients with dementia as compared to the standard clinical and neuropsychological assessment methods. The analysis will focus on the software’s ability to accurately differentiate between dementia and non-dementia cases using brainwave-derived cognitive biomarkers.
时间窗: The outcome measures will be assessed at baseline pre-intervention, 4 weeks, and 8 weeks to examine the diagnostic consistency, reproducibility, and longitudinal stability of NEMA AI’s EEG-based analytical model across multiple patient evaluations.
次要结局
- To validate & publish a study with outcome of new model developed in the field of AI and health.(6 month study and publication timeline)
研究者
Nidhi Nidhi
NEMA AI