GRAPE: 一个多模式数据集的纵向跟踪视野和眼底图像,用于格洛科马管理
Xiaoling Huang1, Xiangyin Kong2, Ziyan Shen3
1Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, China.
Scientific data
|August 5, 2023
概括
这项研究介绍了GRAPE数据集,这是一个新的纵向资源,用于青光眼的研究. 它使人工智能模型能够预测视野进展,从而推进青光眼的管理.
科学领域:
- 眼科医生 眼科 眼科
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 青光眼是不可逆转失明的主要原因,需要长期监测患者和准确的预后.
- 目前的公共玻璃眼数据集主要是横截面的,这限制了对疾病进展的预测AI模型的开发.
- 现有的数据集通常集中在光盘细分和青光眼诊断上,而不是纵向视野变化.
研究的目的:
- 引入眼现实世界评估进展数据集 (GRAPE) 数据集,这是眼研究的新型纵向数据集.
- 促进人工智能 (AI) 模型的开发和验证,以预测青光眼患者的视野进展.
- 为了支持人工智能驱动的青光眼管理和患者护理方面的进步.
主要方法:
- 策划了GRAPE数据集,包括来自263只眼睛的1115个随访记录.
- 数据包括视野 (VF), fundus 图像,光学连贯性断层扫描 (OCT) 测量和临床信息.
- 提供了光盘 (OD) 分段和VF进展的注释.
主要成果:
- 格雷普数据集支持对青光眼进展的纵向分析.
- 基线AI模型在使用数据集预测视野 (VF) 及其进展方面证明了可行性.
- 数据集的结构适合用于训练用于青光眼预后的深度学习模型.
结论:
- GRAPE数据集是人工智能对玻璃眼的研究的一个有价值的公共资源.
- 这一纵向数据集将大大提高对视野进展在绿眼管理中的预测.
- 预计GRAPE的可用性将加速人工智能驱动的洞察力,以了解青光眼的长期发展轨迹.
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