Machine Learning to Predict Outcomes of Anti-VEGF Therapy in Neovascular Age-Related Macular Degeneration.

OBJECTIVE: To develop machine learning models using OCT fluid metrics to predict long-term anti-VEGF treatment intensity and visual acuity (VA) outcomes in 2 large clinical data sets of patients with neovascular age-related macular degeneration (nAMD). DESIGN: Retrospective, multicenter cohort study. SUBJECTS: The study included a total of 2922 eyes from 2475 patients with nAMD treated at 2 tertiary centers in the United Kingdom (Belfast) and Israel (Tel Aviv Medical Center). METHODS: Longitudinal clinical data and OCT scans obtained at baseline and 6 months after treatment initiation were analyzed using a validated deep learning algorithm to extract quantitative volumetric retinal fluid measurements. Machine learning ensemble models were trained using the Belfast data set to predict total anti-VEGF injections and absolute VA at 1, 2, and 3 years from treatment initiation. External validation was performed by applying the trained models to an independent cohort to assess generalizability across health care systems. MAIN OUTCOME MEASURES: Prediction accuracy for the total number of anti-VEGF injections and absolute VA at years 1 to 3 since treatment initiation, assessed by agreement with observed outcomes. RESULTS: In the main cohort, prediction of total anti-VEGF injections was accurate, with 99.6%, 95.5%, and 97.1% of eyes predicted within 2 injections of observed values at years 1, 2, and 3, respectively. Visual acuity predictions were within ≤0.2 logarithm of the minimum angle of resolution units (≤2 ETDRS lines) for 92.5%, 73.5%, and 76.5% of eyes at the corresponding time points. In the external cohort, model performance was reduced, with 45.0% to 53.8% of injection predictions and 26.3% to 41.1% of VA predictions meeting accuracy thresholds. Subretinal fluid at 6 months was the primary driver of injection burden predictions, whereas intraretinal fluid was most influential for VA outcomes. CONCLUSIONS: Machine learning models based on automated fluid metrics can accurately predict treatment burden and VA outcomes within the clinical environment in which they are developed. Although performance declined with external validation, reflecting differences in treatment policies and health care systems, locally trained models remain clinically valuable. Early identification of high- and low-burden treatment trajectories may support individualized decision-making, patient counseling, and more efficient allocation of ophthalmology resources in the growing nAMD population. FINANCIAL DISCLOSURES: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.