Nazlee Zebardast

Abstract The genetic architecture of primary open-angle glaucoma (POAG) remains largely unexplained due to the reliance of previous genome-wide association studies (GWAS) on imprecise phenotypes from electronic health records. We overcome this with a disease-trained, task-transfer machine learning (ML) framework that learns glaucoma-related damage patterns from a clinical repository of 18,985 optical coherence tomography (OCT) scans from 8323 glaucoma patients.

Age-related macular degeneration (AMD) represents a significant public health challenge, with an estimated 288 million cases projected globally by 2040.1 Vision loss from AMD affects critical activities such as reading, mobility, and driving, while also contributing to emotional and psychological challenges, including depression.2 Socioeconomic disparities in health care access and utilization have compounded the burden of vision-threatening AMD (VT-AMD), consisting of exudative AMD and...

Abstract Glaucoma is the leading cause of irreversible blindness globally. Research indicates a disproportionate impact of glaucoma on racial and ethnic minorities. Existing deep learning models for glaucoma detection might not achieve equitable performance across diverse identity groups. We developed fair identify normalization (FIN) module to equalize the feature importance across different identity groups to improve model performance equity.