Glaucoma is a top cause of irreversible blindness globally, making early detection and longitudinal follow-up pivotal to preventing permanent vision loss. Current screening and progression assessment, however, rely on single tests or loosely linked examinations, introducing subjectivity and fragmented care. Limited access to high-quality imaging tools and specialist expertise further compromises consistency and equity in real-world use. To address these gaps, we developed Fair-Eye Net, a fair, reliable multimodal AI system closing the clinical loop from glaucoma screening to follow-up and risk alerting. It integrates fundus photos, OCT structural metrics, VF functional indices, and demographic factors via a dual-stream heterogeneous fusion architecture, with an uncertainty-aware hierarchical gating strategy for selective prediction and safe referral. A fairness constraint reduces missed diagnoses in disadvantaged subgroups. Experimental results show it achieved an AUC of 0.912 (96.7% specificity), cut racial false-negativity disparity by 73.4% (12.31% to 3.28%), maintained stable cross-domain performance, and enabled 3-12 months of early risk alerts (92% sensitivity, 88% specificity). Unlike post hoc fairness adjustments, Fair-Eye Net optimizes fairness as a primary goal with clinical reliability via multitask learning, offering a reproducible path for clinical translation and large-scale deployment to advance global eye health equity.

翻译：青光眼是全球不可逆性失明的主要原因之一，早期发现与长期随访对于预防永久性视力丧失至关重要。然而，目前的筛查与进展评估依赖于单项检查或松散关联的检测，引入了主观性与碎片化诊疗。高质量成像工具与专科医师资源的可及性有限，进一步影响了实际应用中的一致性与公平性。为应对这些不足，我们开发了Fair-Eye Net——一个公平、可靠的多模态人工智能系统，实现了从青光眼筛查到随访及风险预警的临床闭环。该系统通过双流异质融合架构，整合眼底照相、OCT结构参数、视野功能指标及人口统计学因素，并采用不确定性感知的分层门控策略进行选择性预测与安全转诊。公平性约束降低了弱势亚组的漏诊率。实验结果表明，该系统实现了0.912的AUC（特异性96.7%），将种族间的假阴性差异降低了73.4%（从12.31%降至3.28%），保持了稳定的跨域性能，并能提供3–12个月的早期风险预警（敏感性92%，特异性88%）。与事后公平性调整不同，Fair-Eye Net通过多任务学习将公平性作为首要目标与临床可靠性共同优化，为临床转化及大规模部署提供了可复现的路径，以促进全球眼健康公平。