表1_使用基于光学相干断层扫描的机器学习评估中度冠状动脉狭窄中的血流储备分数.DOCX

数据集获取（Figshare）

• 源站标题：Table_1_Assessment of fractional flow reserve in intermediate coronary stenosis using optical coherence tomography-based machine learning.DOCX
• DOI：10.3389/fcvm.2023.1082214.s001
• 发布日期：2023-01-25
• 文件数：1 个
• 文件总体积：168.09 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Assessment_of_fractional_flow_reserve_in_intermediate_coronary_stenosis_using_optical_coherence_tomography-based_machine_learning_DOCX/21951755

命令行下载

curl -L -o "Table_1_Assessment of fractional flow reserve in intermediate coronary stenosis using optical coherence tomography-based machine learning.DOCX" "https://ndownloader.figshare.com/files/38943218"

文件清单

• Table_1_Assessment of fractional flow reserve in intermediate coronary stenosis using optical coherence tomography-based machine learning.DOCX（168.09 KB）

文件总体积 168.09 KB（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

ObjectivesThis study aimed to evaluate and compare the diagnostic accuracy of machine learning (ML)- fractional flow reserve (FFR) based on optical coherence tomography (OCT) with wire-based FFR irrespective of the coronary territory.
BackgroundML techniques for assessing hemodynamics features including FFR in coronary artery disease have been developed based on various imaging modalities. However, there is no study using OCT-based ML models for all coronary artery territories.
MethodsOCT and FFR data were obtained for 356 individual coronary lesions in 130 patients. The training and testing groups were divided in a ratio of 4:1. The ML-FFR was derived for the testing group and compared with the wire-based FFR in terms of the diagnosis of ischemia (FFR ≤ 0.80).
ResultsThe mean age of the subjects was 62.6 years. The numbers of the left anterior descending, left circumflex, and right coronary arteries were 130 (36.5%), 110 (30.9%), and 116 (32.6%), respectively. Using seven major features, the ML-FFR showed strong correlation (r = 0.8782, P OCT-based ML-FFR showed good diagnostic performance in predicting FFR irrespective of the coronary territory. Because the study was a small-size study, the results should be warranted the performance in further large-scale research.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fcvm.2023.1082214.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Assessment_of_fractional_flow_reserve_in_intermediate_coronary_stenosis_using_optical_coherence_tomography-based_machine_learning_DOCX/21951755）。