Embedded AI for neurodegenerative disease monitoring

Full Description

Accurate tracking of symptoms and progression of multiple sclerosis is essential for drug discovery and disease management.

However, current measurement tools are tedious, prone to bias, and do not reflect what people with MS experience.

Many symptoms remain invisible and unrecognised.

Of these, fatigue is the most debilitating and reported by most patients.

Yet no methodology to measure and tackle fatigue exists.

Supervisory team

Paul Patras and Thanasis Tsanas

Project Partners

Hoffmann-La Roche

Project Background

Multiple Sclerosis (MS) is a neurodegenerative autoimmune disease that affects approximately 3 million people worldwide.

The disease primarily affects the central nervous system, with the immune system attacking the myelin sheath around nerve cells.

The symptoms of MS very broadly and can have debilitating effects.

This includes loss of vision, numbness, mobility problems, cognitive decline, etc., which increase as the disease progresses.

Recent studies report that the annual cost of MS-related disability exceeds per capita gross domestic product (GDP), which confirms the major societal cost of this condition.

Project Aims

1. develop new fine-grained data-driven fatigue monitoring methods that build upon detailed telemetry that will be gathered using wearable devices and personal living space sensors (motion, pressure, LiDAR, etc.)

2. data labelling using correlation analysis with image biomarkers, fluid biomarkers, and patient-reported outcomes

3. baselining with volunteer patients using medical-grade wearable devices

4. develop deep learning models that can analyse multi-modal spatio-temporal data to detect early disease-specific symptoms, health improvements, or decline.

5. develop lightweight compact/approximate data structures and deep learning models that can be deployed on computationally-constrained devices

Translational Potential and Expected Impact

Ultimately the outcomes of the project seek to improve fatigue management and potentially the efficacy assessment of new drugs. Long-term, the methods developed may assist consultant neurologists in exploring personalised treatment and improve long-term patient outcomes.