使用机器学习预测中心性浆液性脉络膜视网膜病变复发数据表1

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_Predicting Central Serous Chorioretinopathy Recurrence Using Machine Learning.DOCX
• DOI：10.3389/fphys.2021.649316.s001
• 发布日期：2021-11-25
• 文件数：1 个
• 文件总体积：1.47 MB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Predicting_Central_Serous_Chorioretinopathy_Recurrence_Using_Machine_Learning_DOCX/17080415

命令行下载

curl -L -o "Data_Sheet_1_Predicting Central Serous Chorioretinopathy Recurrence Using Machine Learning.DOCX" "https://ndownloader.figshare.com/files/31578566"

文件清单

• Data_Sheet_1_Predicting Central Serous Chorioretinopathy Recurrence Using Machine Learning.DOCX（1.47 MB）

文件总体积 1.47 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

Purpose: To predict central serous chorioretinopathy (CSC) recurrence 3 and 6 months after laser treatment by using machine learning.
Methods: Clinical and imaging features of 461 patients (480 eyes) with CSC were collected at Zhongshan Ophthalmic Center (ZOC) and Xiamen Eye Center (XEC). The ZOC data (416 eyes of 401 patients) were used as the training dataset and the internal test dataset, while the XEC data (64 eyes of 60 patients) were used as the external test dataset. Six different machine learning algorithms and an ensemble model were trained to predict recurrence in patients with CSC. After completing the initial detailed investigation, we designed a simplified model using only clinical data and OCT features.
Results: The ensemble model exhibited the best performance among the six algorithms, with accuracies of 0.941 (internal test dataset) and 0.970 (external test dataset) at 3 months and 0.903 (internal test dataset) and 1.000 (external test dataset) at 6 months. The simplified model showed a comparable level of predictive power.
Conclusion: Machine learning achieves high accuracies in predicting the recurrence of CSC patients. The application of an intelligent recurrence prediction model for patients with CSC can potentially facilitate recurrence factor identification and precise individualized interventions.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fphys.2021.649316.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Predicting_Central_Serous_Chorioretinopathy_Recurrence_Using_Machine_Learning_DOCX/17080415）。