Structure–function correlation between qOCT biomarkers of GA area and VA. (a) A random forest regression model was trained using the deep-learning segmentation output (i.e. the raw probabilities at the voxel level for each feature (RPE-loss, photoreceptor degeneration, hypertransmission, and RORA) as input variables to predict cross-sectional VA under standard luminance conditions, low-luminance VA, and low-luminance deficit in ETDRS letters. For VA under standard-luminance conditions, separate models were evaluated for: (i) BCVA under RCT conditions i.e., FILLY; (ii) VA from real-world routine care, i.e., MEH; and (iii) a third that combines the two. Model bootstrapped 100-fold with resultant regression coefficients (r2) and mean absolute error (MAE) shown. Importance of qOCT biomarker features in predicting (b) standard visual acuity and (c) low luminance visual acuity was queried using machine learning. Random forests modelling was used to evaluate value of the qOCT biomarkers RPE-loss, photoreceptor degeneration, hypertransmission, and RORA in predicting cross-sectional visual acuity under standard lighting conditions (Overall model). The resultant adjusted feature importance values were summed according to location within ETDRS region and multiplied by 100 to give the percentage contribution towards the model's performance. For example, RORA within the foveal region accounted for 16.8% of the model’s performance of r2 0.40 MAE 11.7 ETDRS letters for standard visual acuity.
