Predicting Incremental and Future Visual Change in Neovascular Age-Related Macular Degeneration Using Deep Learning

Purpose To evaluate the predictive usefulness of quantitative imaging biomarkers, acquired automatically from OCT scans, of cross-sectional and future visual outcomes of patients with neovascular age-related macular degeneration (AMD) starting anti–vascular endothelial growth factor (VEGF) therapy. Design Retrospective cohort study. Participants Treatment-naive, first-treated eyes of patients with neovascular AMD between 2007 and 2017 at Moorfields Eye Hospital (a large, United Kingdom single center) undergoing anti-VEGF therapy. Methods Automatic segmentation was carried out by applying a deep learning segmentation algorithm to 137 379 OCT scans from 6467 eyes of 3261 patients with neovascular AMD. After applying selection criteria, 926 eyes of 926 patients were analyzed. Main Outcome Measures Correlation coefficients (R2 values) and mean absolute error (MAE) between quantitative OCT (qOCT) parameters and cross-sectional visual function, as well as the predictive value of these parameters for short-term visual change, that is, incremental visual acuity (VA) resulting from an individual injection, as well as VA at distant time points (up to 12 months after baseline). Results Visual acuity at distant time points could be predicted: R2 = 0.80 (MAE, 5.0 Early Treatment Diabetic Retinopathy Study [ETDRS] letters) and R2 = 0.7 (MAE, 7.2 ETDRS letters) after injection at 3 and at 12 months after baseline (P Conclusions Automatic segmentation enables rapid acquisition of quantitative and reproducible OCT biomarkers with potential to inform treatment decisions in the care of neovascular AMD. This furthers development of point-of-care decision-aid systems for personalized medicine.