青光眼机器学习进展范围综述数据表1

数据集获取（Figshare）

• 源站标题：Data Sheet 1_A scoping review of advancements in machine learning for glaucoma: current trends and future direction.zip
• DOI：10.3389/fmed.2025.1573329.s001
• 发布日期：2025-04-24
• 文件数：1 个
• 文件总体积：8.57 MB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_A_scoping_review_of_advancements_in_machine_learning_for_glaucoma_current_trends_and_future_direction_zip/28855232

命令行下载

curl -L -o "Data Sheet 1_A scoping review of advancements in machine learning for glaucoma: current trends and future direction.zip" "https://ndownloader.figshare.com/files/53950697"

文件清单

• Data Sheet 1_A scoping review of advancements in machine learning for glaucoma: current trends and future direction.zip（8.57 MB）

文件总体积 8.57 MB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

IntroductionMachine learning technology has demonstrated significant potential in glaucoma research, particularly in early diagnosis, predicting disease progression, evaluating treatment responses, and developing personalized treatment strategies. The application of machine learning not only enhances the understanding of the pathological mechanism of glaucoma and optimizes the diagnostic process but also provides patients with accurate medical services.
MethodsThis study aimed to describe the current state of research, highlight directions for further development, and identify potential trends for improvement. This review was conducted following the scoping review of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension to showcase advancements in the application of machine learning in glaucoma research and treatment.
ResultsWe employed a comprehensive search strategy to retrieve literature from the Web of Science Core Collection database, ultimately including 3,581 articles in the analysis. Through data analysis, we identified current research hotspots, noted differences in researchers’’ attitudes and opinions, and predicted potential future development trends.
DiscussionWe divided the research topics into six categories, clearly identifying “eye diseases”, “retinal fundus imaging” and “risk factors” as the key terms for the development of this field. These findings signify the promising prospects of machine learning, particularly when integrated with multimodal technologies and large language models, to enhance the diagnosis and treatment of glaucoma.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fmed.2025.1573329.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_A_scoping_review_of_advancements_in_machine_learning_for_glaucoma_current_trends_and_future_direction_zip/28855232）。