GRAPE: A multi-modal dataset of longitudinal follow-up visual field and fundus images for glaucoma management

Abstract

AbstractAs one of the leading causes of irreversible blindness worldwide, glaucoma is characterized by structural damage and functional loss. Glaucoma patients often have a long follow-up and prognosis prediction is an important part in treatment. However, existing public glaucoma datasets are almost cross-sectional, concentrating on segmentation on optic disc (OD) and glaucoma diagnosis. With the development of artificial intelligence (AI), the deep learning model can already provide accurate prediction of future visual field (VF) and its progression with the support of longitudinal datasets. Here, we proposed a public longitudinal glaucoma real-world appraisal progression ensemble (GRAPE) dataset. The GRAPE dataset contains 1115 follow-up records from 263 eyes, with VFs, fundus images, OCT measurements and clinical information, and OD segmentation and VF progression are annotated. Two baseline models demonstrated the feasibility in prediction of VF and its progression. This dataset will advance AI research in glaucoma management.