SCIENCE
PROBLEM
6.5 million Americans are living with Alzheimer’s disease.
By 2050, this number is projected to rise to nearly 13 million Current Alzheimer’s testing is:
• Lengthy
• Invasive
• Expensive
• Imprecise
EXISTING ALTERNATIVES
- Screening tests shorter than a cognitive battery (MoCA, MMSE), but not sensitive to the earliest signs of cognitive decline, and not designed to distinguish the most likely etiology (e.g., Alzheimer’s disease).
- Neuropsychological cognitive assessment, which is highly time consuming and requires expertise in interpretation of results, making their integration into existing standards of care infeasible.
- •Confirmatory biomarker testing with PET or cerebrospinal fluid (CSF) testing is expensive, invasive, and time-consuming
SOLUTIONS
• A more convenient test for precise diagnosis of
Alzheimer’s disease at the early stage.
• A more cost-effective test with instantaneous results
powered by AI.
• A more accessible and ubiquitous test.
COMPETITIVE ADVANTAGE
- Artificial Intelligence model
- Fractal model: A model to capture the loss of complexity of the retinal vasculature due to disease.
- Electroretinogram (ERG) model: An approach that takes advantage of electroretinography as a dynamic metric to provide a higher probability in identifying those with early Alzheimer’s disease.
- A multivariate biomarker-based solution: A multivariate approach based on a proprietary algorithm that integrates static measures (fractals) and dynamic metrics (ERGs) to power an AI model for accurate diagnosis of early Alzheimer’s disease.