Fundus Camera Module for Early Detection of Alzheimer's Disease

I.R.C.C.S. Fondazione Santa Lucia80 enrolled

Overview

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.

Study Type

OBSERVATIONAL

Enrollment

Conditions

Alzheimer Disease

Interventions

Fundoscopic ExamDIAGNOSTIC_TEST

visualization of the retina using an ophthalmoscope

Eligibility

Medical Language ↔ Plain English

Inclusion Criteria: * 55 years or older * Diagnosis: 1. 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. 2. 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 Exclusion Criteria: * Severe cognitive impairment (MMSE \<10) preventing cooperation during the imaging process. * Inability to remain still during the fundus imaging procedure.

Outcomes

Primary Outcomes

Validation of Multispectral Fundus Imaging for Alzheimer's Disease Detection

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.

Time frame: Baseline