Hillel Yaffe Glaucoma Dataset (HYGD): A Gold-Standard Annotated Fundus Dataset for Glaucoma Detection

Glaucomatous optic neuropathy (GON) is a leading cause of irreversible
blindness worldwide, affecting an estimated 64.3 million people globally with
projections reaching 111.8 million by 2040. Approximately 50% of cases remain
undiagnosed until advanced stages when vision loss becomes noticeable.
Traditional diagnosis requires comprehensive ophthalmic examinations by
specialists, creating accessibility barriers in many regions.

The Hillel Yaffe Glaucoma Dataset (HYGD) addresses a critical limitation in
existing GON datasets: the lack of gold-standard annotations. Unlike most
publicly available datasets where glaucoma labels are determined solely from
digital fundus images (DFIs), HYGD's labels are based on comprehensive
ophthalmic examinations, including visual acuity assessment, intraocular
pressure measurement, optical coherence tomography (OCT), visual field tests,
and at least one year of follow-up monitoring.

The dataset is structured to include both the DFIs and a labels file
containing patient IDs, GON classifications, and image quality scores. All
DFIs were taken using a TOPCON DRI OCT Triton retinal camera with a 45° FOV
and underwent deidentification and standardization processing.

HYGD enables researchers to train and benchmark models on rigorously annotated
data, potentially improving their reliability. This dataset serves as a
valuable resource for developing generalizable models that can function across
diverse patient populations and clinical settings, ultimately supporting
earlier detection and treatment of GON.