ArticleScientific data2023
GRAPE: A multi-modal dataset of longitudinal follow-up visual field and fundus images for glaucoma management.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- A Systematic Review of Deep Learning and Machine Learning Applications in Longitudinal Multimodal Clinical Data.Journal of healthcare informatics research · 2026Review
- Dynamic Oculomics for Diagnostic Monitoring: Personal Ocular Trends, Risk Stratification and Treatment Response.Diagnostics (Basel, Switzerland) · 2026Review
- Comparative evaluation of large language models and clinicians in real-world glaucoma clinical reasoning.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Article
- A generalizable eye disease detection method based on Zero-Shot Learning.Communications medicine · 2026Article
- Matters Arising: Near-identical images, not foundation models, explain purported re-identification of patients from medical imaging.NPJ digital medicine · 2026Article
- A Structure-Function Dataset With Known Rates of Glaucomatous Progression.Translational vision science & technology · 2026Article
- Longitudinal Forecasting of Retinal Structure and Function Using a Multimodal StyleGAN-Based Architecture.Bioengineering (Basel, Switzerland) · 2026Article
- Two-Stage Decoupling Framework for Variable-Length Glaucoma Prognosis.Learning with longitudinal medical images and data : first International Workshop, LMID 2025, held in conjunction with MICCAI 2025, Daejeon, South Korea, September 27, 2025, Proceedings. International Workshop on Learning with Longitudi... · 2026Article
- Benchmarking Vision Encoders for Image Classification in Ophthalmology.Computational and structural biotechnology journal · 2026Article
- Real-world data landscape for glaucoma in Europe: a questionnaire-based analysis of resources among European Glaucoma Society members.BMJ open ophthalmology · 2025Article
- Predicting Pattern Standard Deviation in Glaucoma: A Machine Learning Approach Leveraging Clinical Data.Vision (Basel, Switzerland) · 2025Article
- Re-identification of patients from imaging features extracted by foundation models.NPJ digital medicine · 2025Article
- Interpretable longitudinal glaucoma visual field estimation deep learning system from fundus images and clinical narratives.NPJ digital medicine · 2025Article
- The Role of Artificial Intelligence in Predicting the Progression of Intraocular Hypertension to Glaucoma.Life (Basel, Switzerland) · 2025Article
- "Weibing" in traditional Chinese medicine-biological basis and mathematical representation of disease-susceptible state.Acta pharmaceutica Sinica. B · 2025Review
- Equitable artificial intelligence for glaucoma screening with fair identity normalization.NPJ digital medicine · 2025Article
- Advances and prospects of multi-modal ophthalmic artificial intelligence based on deep learning: a review.Eye and vision (London, England) · 2024Review
- Applicability of Oculomics for Individual Risk Prediction: Repeatability and Robustness of Retinal Fractal Dimension Using DART and AutoMorph.Investigative ophthalmology & visual science · 2024Article
- A fundus image dataset for intelligent retinopathy of prematurity system.Scientific data · 2024Article
- GRAPE: A multi-modal dataset of longitudinal follow-up visual field and fundus images for glaucoma management.Scientific data · 2023Article
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Authors and funding
7 authors.
Funding
Abstract
As 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.
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Registered trials
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