AI-GUIDED ENDPOINT SELECTION FOR NEUROPROTECTION TRIALS IN GLAUCOMA.

Standard Automated Perimetry (SAP) is the mainstay for monitoring glaucoma progression and has been accepted by the U.S. Food and Drug Administration (FDA) as a trial endpoint, but only under stringent criteria of ≥7 dB loss in five pre-specified test locations. Identifying such locations a priori has remained a major barrier for neuroprotection trials. We developed an attention-based graph neural network (GNN) to predict the visual field points most likely to deteriorate (High-5) using baseline SAP data. The model was trained in the Bascom Palmer Ophthalmic Registry (BPOR; 6,996 eyes, 5,405 patients, 40,914 tests) and externally validated in the Duke Glaucoma Registry (DGR; 5,211 eyes, 3,933 patients, 31,225 tests) and the University of Washington Humphrey Visual Field dataset (UWHVF; 2,030 eyes, 1,195 patients, 10,310 tests). In internal validation, the mean slope at High-5 points among progressors was -2.16±0.80 dB/year, compared to -0.55±0.44 dB/year for Low-5 and -1.02±0.40 dB/year for mean deviation (MD). Similar results were observed in DGR (-2.05 vs -0.45 vs -0.93 dB/year) and UWHVF (-2.32 vs -0.66 vs -1.14 dB/year). High-5 showed superior discrimination of progressors from non-progressors with areas under the ROC curve of 0.883, 0.898, and 0.937 across the three cohorts, consistently outperforming MD (0.871-0.911) and Low-5 (0.668-0.731). Nearly all progressing eyes exhibited a repeatable Funding: This work was supported in part by NIH R01 (EY036593) and by the Glaucoma Research Foundation (grant Endpoints2025MedeF).