Accelerating Functional Endpoints in Geographic Atrophy Trials via Morphology-Based Perimetry Grids

Purpose. To evaluate whether a Geographic Atrophy Morphology-based Mapping Algorithm (GAMMA) grid, informed by geographic atrophy (GA) lesion morphology, can accelerate functional progression detection compared with a conventional 10-2 grid and a dense grid (129 locations). This work is motivated by emerging regulatory expectations requiring at least five locations to worsen by [≥]7 dB from baseline. Methods. Binary atrophy masks from six autofluorescence images were used to simulate GA expansion over 3 years at 3-month intervals using a stochastic perimeter-growth model with a fixed preferential expansion direction (Pdir). For each image, 32 independent growth histories and 32 microperimetric test realisations per history were generated. For each grid (10-2, Dense, and GAMMA), 5-point clusters were selected outside the baseline GA lesion along three directions (0{degrees}, 30{degrees}, 120{degrees}) away from Pdir, simulating full, partial, and no prior knowledge of Pdir. Ground-truth sensitivities were <0 dB inside the GA lesion and normal outside, calculated using a published normative equation. Response variability was simulated following Henson et al. with baseline averaging. Detection time was the first visit at which all five selected locations showed [≥]7 dB loss from baseline. Survival curves and median detection times (T50) were used to compare grid performance. Results. The GAMMA grid achieved the earliest progression detection across all scenarios. Under full knowledge of the expansion direction, T50 was 1.0 year for GAMMA versus 1.25 and 1.5 years for Dense and 10-2, respectively. With partial knowledge, GAMMA's T50 was 1.25 years versus 1.5 and 2.0 years for Dense and 10-2. Even under no knowledge, GAMMA detected progression earliest (T50 = 1.5 years), while Dense required 6 months longer and 10-2 nearly double the time (2.75 years). Conclusions. The automatic GAMMA grid accelerates detection of localised functional progression compared with conventional and dense grids. Structure-informed grid optimisation may better align testing with likely expansion paths, potentially reducing follow-up duration and sample sizes in perimetry-based interventional trials.