AI-Enhanced Corneal Confocal Microscopy for Early Detection and Monitoring of Neurodegeneration

Background & Unmet Need

• Dementia affects 40-50 million people worldwide, with MCI diagnosis challenging due to insidious onset and cognitive decline influenced by age, education, and cultural factors
• Current AD biomarkers (amyloid-PET, CSF analysis, brain MRI) are limited by high cost, invasiveness, radiation exposure, and inability to distinguish MCI from normal cognition
• Corneal confocal microscopy (CCM) is a rapid, non-invasive imaging technique that captures high-resolution images of corneal nerves using laser scanning technology
• Dr. Malik and his team has previously demonstrated they could visualize corneal nerve loss in diabetic neuropathy 
• Unmet Need: Accurate, reliable, non-invasive, and inexpensive method for early detection of neurodegeneration and monitoring disease progression in MCI and dementia

Technology Overview

• The Technology: AI-enhanced CCM platform that automatically quantify corneal nerve architecture
• Deep learning convolutional neural networks automatically extract and analyze corneal nerve features without requiring manual feature engineering or pre-processing steps
• The Discovery: In a study of 182 subjects, progressive reduction in corneal nerve fiber density, branch density, and length correlated with cognitive decline from normal through MCI to dementia
• PoC Data: CCM achieved 78-86% diagnostic accuracy for distinguishing MCI from healthy controls versus 40-53% for medial temporal atrophy, with 85% accuracy for dementia detection comparable to brain MRI (92% accuracy)
• Longitudinal data shows accelerated corneal nerve loss in MCI (7.8% annually) and dementia (11.2% annually) versus normal aging (3.1% annually), demonstrating disease progression monitoring

Technology Applications

• Early diagnostic and prognostic tool for MCI detection and identifying progression risk to dementia
• Therapeutic monitoring for assessing treatment efficacy in neurodegenerative disease trials
• Broader neurological applications for diagnostic assessment in Parkinson's disease, multiple sclerosis, autism, and epilepsy

Technology Advantages

• Non-invasive, rapid, and cost-effective with no radiation exposure or painful procedures required
• Superior early detection capability for MCI compared to current brain imaging methods
• Objective quantification reduces variability and enables standardized assessment across clinical sites