HYAMD High-Resolution Fundus Image Dataset for age related macular degeneration (AMD) Diagnosis

TL;DR

The HYAMD dataset is a comprehensive, high-resolution, longitudinal collection of fundus images with clinical annotations, aimed at advancing machine learning-based diagnosis of age-related macular degeneration (AMD).

Contribution

It introduces the first open-access Israeli retinal dataset with detailed clinical labels for AMD, supporting machine learning research in ophthalmology.

Findings

Provides 1,560 high-resolution fundus images with clinical labels.

Includes longitudinal data from 325 patients across multiple years.

Supports development of machine learning models for AMD diagnosis.

Abstract

The Hillel Yaffe Age Related Macular Degeneration (HYAMD) dataset is a longitudinal collection of 1,560 Digital Fundus Images (DFIs) from 325 patients examined at the Hillel Yaffe Medical Center (Hadera, Israel) between 2021 and 2024. The dataset includes an AMD cohort of 147 patients (aged 54-94) with varying stages of AMD and a control group of 190 diabetic retinopathy (DR) patients (aged 24-92). AMD diagnoses were based on comprehensive clinical ophthalmic evaluations, supported by Optical Coherence Tomography (OCT) and OCT angiography. Non-AMD DFIs were sourced from DR patients without concurrent AMD, diagnosed using macular OCT, fluorescein angiography, and widefield imaging. HYAMD provides gold-standard annotations, ensuring AMD labels were assigned following a full clinical assessment. Images were captured with a DRI OCT Triton (Topcon) camera, offering a 45 deg field of view and 1960 x 1934 pixel resolution. To the best of our knowledge, HYAMD is the first open-access retinal dataset from an Israeli sample, designed to support AMD identification using machine learning models.