Ellipsoid zone (EZ) integrity metrics and hyperreflective foci (HRF) counts are strong baseline predictors of intermediate AMD progression to advanced atrophic AMD over 2 years.
Machine learning-enhanced quantitative OCT analysis effectively identifies subtle retinal structural changes not apparent on conventional imaging.
A predictive random forest model using multiple OCT biomarkers achieved high accuracy (AUC = 0.85) in forecasting disease progression.
These findings underscore the potential for early risk stratification, informing clinical trial design and targeted therapeutic interventions to delay atrophic AMD onset.
