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

Sayeh Pourjavan,1,2 Niki Nazaran,3 Tom Vaucourt,3 Nasim Abdouli,1 Adil El Maftouhi,4 Benoit Macq3 1Department of Ophthalmology, Cliniques Universitaires Saint Luc, UCLouvain, Brussels, Belgium; 2Department of Ophthalmology, Chirec Hospital Group, Site Delta, Brussels, Belgium; 3Institute for Information and Communication Technologies, Electronics, and Applied Mathematics (ICTEAM), Louvain School of Engineering, UCLouvain, Louvain-la-Neuve, Belgium; 4Centre Ophtalmologique de RIVE, Geneve, SwitzerlandCorrespondence: Sayeh Pourjavan, Email [email protected]: 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 R² under subject-level grouped cross-validation.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 (R² = 0.268), while the structural OCT-only model performed poorly (R² = 0.296). Combining structural and vascular features achieved a highly accurate age prediction model (R² = 0.895; RMSE = 5.025 years and MAE of 4.024) under subject-level cross-validation.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.Keywords: glaucoma, OCTA, optic disc, macula, vessel density, OCTA normative values, GlauOCTA, GlauOCTA-AI, neurovascular degeneration