Fundus Camera Module for Early Detection of Alzheimer's Disease
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 80
- 试验地点
- 1
- 主要终点
- Validation of Multispectral Fundus Imaging for Alzheimer's Disease Detection
研究概览
简要总结
Alzheimer's Disease (AD) affects tens of millions of people just in Europe. It is typically detected in its late stage when irreversible damage has already occurred, and current treatments are mostly conservative or palliative. Here we developed a device performing high-resolution multispectral imaging of the eye fundus to detect AD in its early stage. After clinical testing, the proposed device has a high potential to become a method for routine population screening, as it is non-invasive and affordable. The possibility of AD detection in an early stage is crucial for developing a new pharmaceutical treatment that would dramatically improve the lives of millions and save the expenses connected to lifelong healthcare assistance to people with AD in later stages.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Control
- 时间视角
- Cross Sectional
入排标准
- 年龄范围
- 55 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •55 years or older
- •Diagnosis:
- •For AD group: Clinically diagnosed with Alzheimer's disease (mild to moderate stage) based on Mini-Mental State Examination (MMSE) score between 10-26 and confirmed by PET imaging or CSF.
- •For Control group: No diagnosis of Alzheimer's disease or other neurodegenerative disorders.
- •Signed informed consent by the patient. If there are any doubts that the patient is mentally capable of giving informed consent, the patient will be examined and verified to be mentally capable by an independent physician/ neurologist, prior to the initiation of any study specific procedure
排除标准
- •Severe cognitive impairment (MMSE <10) preventing cooperation during the imaging process.
- •Inability to remain still during the fundus imaging procedure.
结局指标
主要结局
Validation of Multispectral Fundus Imaging for Alzheimer's Disease Detection
时间窗: Baseline
Create an automatic AD detection model using advanced data processing, such as machine learning, that would be able to distinguish between AD and a healthy retina.
次要结局
未报告次要终点