Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, China.
State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, 310013, China.
Sci Data. 2023 Aug 5;10(1):520. doi: 10.1038/s41597-023-02424-4.
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.
作为全球致盲的主要原因之一，青光眼的特征是结构损伤和功能丧失。青光眼患者通常需要长期随访，预后预测是治疗的重要组成部分。然而，现有的公共青光眼数据集几乎都是横截面的，集中在视盘（OD）的分割和青光眼的诊断上。随着人工智能（AI）的发展，深度学习模型在纵向数据集的支持下，已经可以提供对未来视野（VF）及其进展的准确预测。在这里，我们提出了一个公共的纵向青光眼真实评估进展集合（GRAPE）数据集。该数据集包含 263 只眼中的 1115 个随访记录，包含视野、眼底图像、OCT 测量和临床信息，以及 OD 分割和视野进展的注释。两个基线模型证明了在预测视野和其进展方面的可行性。该数据集将推动青光眼管理中人工智能研究的发展。
