Machine-learning and Automatically Segmented Retinal Biomarkers Generate Spatial Heatmaps Predictive for Standard and Low Luminance Visual Acuity in Geographic Atrophy

Objective: Predict visual function with machine-learning applied to automatically acquired quantitative imaging biomarkers in geographic atrophyDesign: Post-hoc analysis of data from a clinical trial and routine clinical care.Methods: Automated segmentation of OCT scans from 476 eyes (325 patients) with geographic atrophy. Machine learning modelling of resultant quantitative OCT (qOCT) biomarkers to predict cross-sectional visual acuity under both standard luminance (VA) and low luminance (LLVA) conditions.Main Outcome Measure: Correlation coefficient (R2) and mean absolute error (MAE) for cross-sectional VA and LLVA in Early Treatment Diabetic Retinopathy Study (ETDRS) letters.Results: Best-corrected VA under both standard luminance (R2 0.46 MAE 10.2 ETDRS letters) and low-luminance conditions (R2 0.25 MAE 12.1) could be predicted. The foveal region contributed the most (46.5%) toward model performance, with retinal pigment epithelium loss and outer retinal atrophy contributing the most (31.1%). For LLVA, however, features in the non-foveal regions were most important (74.5%), led by photoreceptor degeneration (38.9%).Conclusions: Our method of automatic qOCT segmentation demonstrates functional significance for vision in geographic atrophy, including LLVA. LLVA is itself predictive of geographic atrophy progression, implying that the predictive qOCT biomarkers provided by our model are also prognostic.