AI-Based Ocular Age Estimation from Combined OCT and OCTA Metrics: Decade-Stratified Normative Modelling in Healthy Eyes - a Pilot Study.

Purpose: To define decade-stratified normative values for vascular parameters obtained from optical coherence tomography angiography (OCTA) in healthy eyes and to evaluate their utility for predicting biological ocular age using artificial intelligence. Methods: This cross-sectional pilot study included 136 rigorously screened healthy subjects aged 10-80 years. Spectral-domain OCT and OCTA scans were acquired using the Optovue Solix platform. Structural and vascular features were extracted from both the macular and optic disc regions. Vessel density (VD) metrics were calculated in the superficial capillary plexus using the ETDRS grid (macula) and Garway-Heath segmentation (peripapillary). Foveal avascular zone (FAZ) area, FAZ circularity, and FD-300 density were also analysed. Disc and RNFL metrics were included. Age-stratified normative values were derived, and a support vector regression (SVR) model was developed to estimate biological ocular age based on structural-only, vascular-only, and combined imaging inputs. Model performance was evaluated using root mean squared error (RMSE) and Results: Vessel density in the macular and peripapillary regions declined progressively with age, particularly after the fifth decade. FAZ area increased, and circularity decreased with age, while FD-300 density remained relatively stable. The SVR model trained on OCTA-only features showed modest predictive performance ( Conclusion: This pilot study provides decade-stratified normative OCTA metrics and demonstrates that combining OCT and OCTA features significantly enhances AI-based ocular age estimation. These findings offer a promising foundation for early glaucoma risk stratification through biologically meaningful ocular age prediction.