MonoPredict AD

Elevator Pitch: MonoPredict AD leverages cutting-edge machine learning to transform Alzheimer’s disease management by offering a novel way to detect and monitor AD progression accurately, thereby unlocking timely intervention opportunities. Our platform promises to bring new hope to millions by revolutionizing how we approach one of today’s most challenging healthcare crises.

Concept

A machine learning-based monitoring and predictive platform for early Alzheimer’s disease detection and progression tracking.

Objective

To improve early detection and monitoring of Alzheimer’s disease progression using a novel machine learning model with a monotonicity constraint.

Solution

Leverage longitudinal MRI and amyloid-PET imaging data to train ML models that predict and track AD’s progression in a consistent and ordered manner across follow-up visits.

Revenue Model

Subscription-based access for healthcare providers and research institutions, alongside pay-per-assessment fees for individuals.

Target Market

Neurologists, geriatric specialists, hospitals, Alzheimer’s research institutions, and potentially individuals at high risk or early stages of AD.

Expansion Plan

Initially focusing on healthcare institutions and research bodies, eventually expanding to direct consumer access through partnerships with wearable tech companies for preventive tracking.

Potential Challenges

Complexities in integrating diverse data sources, ensuring patient data privacy and security, technical challenges in model development and deployment.

Customer Problem

Current AD detection methods fail to predict and monitor disease progression accurately and consistently, leading to delayed intervention opportunities.

Regulatory and Ethical Issues

Navigating HIPAA and GDPR for patient data, ethical considerations in predictive modeling accuracy and its impact on patients.

Disruptiveness

Introducing a more accurate, trustworthy, and consistent method for early AD detection and progression tracking can significantly disrupt current diagnostic and monitoring practices.