Supplementary Material for: The British-Israeli Project for Algorithm-Based Management of Age-related Macular Degeneration: Deep Learning Integration for Real- World Data Management and Analysis

Purpose: To describe the development of an integrative dataset, combining clinical and optical coherence tomography (OCT) imaging data by applying a deep learning algorithm for automated, objective, and comprehensive quantification of OCT scans to two large real-world datasets of eyes with neovascular age-related macular degeneration (nAMD). We further report baseline characteristics of the study population, focusing on demographics, clinical parameters, and quantitative retinal morphological features.
Methods: This retrospective study analyzed data from 5,207 eyes of 4,265 nAMD patients treated at two centers in the UK and Israel. Longitudinal clinical data and OCT scans were. A deep learning algorithm (NOATM, Notal Ltd.) was used to quantify retinal fluid volumes and morphological features. Baseline characteristics were compared between the cohorts.
Results: The dataset included 134,340 visual acuity measurements, 79,457 OCT scans, and 73,218 anti-VEGF injections. Median follow-up was 4.54 years (UK) and 3.12 years (Israel). Baseline visual acuity differed significantly between cohorts due to varying treatment criteria. Fluid distribution patterns were similar, with most eyes showing combined intraretinal and subretinal fluid. Age-related trends in fluid volumes were observed. Weak correlations were found between baseline OCT measurements and visual acuity.
Conclusions: This study demonstrates the feasibility of integrating large-scale clinical and imaging data for automated analysis in nAMD. The comprehensive baseline characterization provides insights into real-world presentations and lays the groundwork for enabling personalized decision-making and optimizing outcomes based on individual patient profiles and fluid distribution patterns.