Masataka Sato

Glaucoma is the leading cause of irreversible blindness worldwide and is characterized by progressive retinal ganglion cell (RGC) loss and optic nerve degeneration. While elevated intraocular pressure (IOP) remains the primary modifiable risk factor, a certain proportion of patients continue to deteriorate despite adequate IOP control, pointing to IOP-independent mechanisms of neurodegeneration. Oxidative stress—defined as an imbalance between the production of reactive oxygen species and the capacity of endogenous antioxidant defenses—has emerged as a central, multi-tiered contributor to glaucoma pathogenesis. In the anterior segment, chronic oxidative damage to the trabecular meshwork impairs aqueous humor outflow and drives IOP elevation. In addition, oxidative stress may impair ocular biomechanical integrity, including corneal hysteresis and lamina cribrosa, resulting in heightened susceptibility to IOP fluctuations. In the posterior segment, oxidative stress directly contributes to mitochondrial damage and vascular endothelial injury, leading to RGC apoptosis. The nuclear factor erythroid 2-related factor 2 (Nrf2)/Kelch-like ECH-associated protein 1 (Keap1) pathway coordinates the principal endogenous antioxidant response, while nicotinamide adenine dinucleotide (NAD⁺) depletion links redox imbalance to metabolic vulnerability of RGCs. This narrative review synthesizes evidence published up to March 2026 on the molecular mechanisms of oxidative stress in glaucoma, the role of biomarkers in aqueous humor and systemic circulation, and the translational landscape of antioxidant-based neuroprotection—including nicotinamide, coenzyme Q10, alpha-lipoic acid, and Nrf2-activating compounds. We highlight gaps between preclinical promise and clinical evidence, and outline priorities for future randomized controlled trials.

Glaucoma, a leading cause of blindness, requires accurate early detection. We present an AI-based Glaucoma Screening (AI-GS) network comprising six lightweight deep learning models (total size: 110 MB) that analyze fundus images to identify early structural signs such as optic disc cupping, hemorrhages, and nerve fiber layer defects. The segmentation of the optic cup and disc closely matches that of expert ophthalmologists. AI-GS achieved a sensitivity of 0.9352 (95% CI 0.9277–0.9435) at 95% specificity. In real-world testing, sensitivity dropped to 0.5652 (95% CI 0.5218–0.6058) at ~0.9376 specificity (95% CI 0.9174–0.9562) for the standalone binary glaucoma classification model, whereas the full AI-GS network maintained higher sensitivity (0.8053, 95% CI 0.7704–0.8382) with good specificity (0.9112, 95% CI 0.8887–0.9356). The sub-models in AI-GS, with enhanced capabilities in detecting early glaucoma-related structural changes, drive these improvements. With low computational demands and tunable detection parameters, AI-GS promises widespread glaucoma screening, portable device integration, and improved understanding of disease progression.