基于心电图特征使用人工智能开发冠状动脉疾病诊断模型的范围综述

数据集获取（Figshare）

• 源站标题：Using artificial intelligence in the development of diagnostic models of coronary artery disease based on ECG features: A scoping review
• DOI：10.6084/m9.figshare.29478020.v1
• 发布日期：2025-07-04
• 源站页面：https://figshare.com/articles/dataset/Using_artificial_intelligence_in_the_development_of_diagnostic_models_of_coronary_artery_disease_based_on_ECG_features_A_scoping_review/29478020

源站：https://figshare.com/articles/dataset/Using_artificial_intelligence_in_the_development_of_diagnostic_models_of_coronary_artery_disease_based_on_ECG_features_A_scoping_review/29478020

按页面 Download 按钮获取。

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

Introduction
Coronary Artery Disease (CAD) remains the leading global cause of cardiovascular mortality, with its prevalence demonstrating a significant increase from 1811 to 2549 per 100,000 individuals between 1990 and 2019. Projections further indicate rising CAD mortality rates in low- and middle-income countries by 2050, underscoring its persistent and growing public health burden. Early diagnosis is crucial for mitigating this burden. While coronary angiography is the gold-standard diagnostic method, its invasive nature and dependency on specialized equipment and expertise create barriers to access, particularly in primary care settings and underserved regions. This limitation contributes to diagnostic delays, health inequities, and severe sequelae such as myocardial infarction and heart failure. The 12-lead electrocardiogram (ECG), being non-invasive, low-cost, and widely available, offers immense potential for early CAD detection in resource-constrained environments. However, its utility is hampered by the subtlety of ECG changes indicative of CAD, which can be difficult to discern visually and susceptible to inter-observer variability in interpretation. Artificial Intelligence (AI) emerges as a promising solution to overcome these limitations. An increasing body of research, spanning medicine, computer science, and engineering, has developed AI models using ECG features—ranging from raw signals to extracted morphological characteristics (e.g., PR interval, QRS duration, ST-segment morphology, QTc) and multidimensional features (time, frequency, non-linear domains)—for CAD detection. Initial reports demonstrate high performance metrics (Accuracy: 90%-99.21%; Sensitivity: 91.13%-98.43%; Specificity: 95.88%-100%) using diverse approaches like 1D-Convolutional Neural Networks (1D-CNN), Artificial Neural Networks (ANN) coupled with signal transforms (e.g., Wavelet, Fourier), and Support Vector Machines (SVM) with feature reduction (e.g., PCA). Despite these encouraging laboratory results, there appears to be a critical gap between development and clinical implementation. The clinical relevance, external validation robustness, and translational readiness of t

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.29478020.v1（source: https://figshare.com/articles/dataset/Using_artificial_intelligence_in_the_development_of_diagnostic_models_of_coronary_artery_disease_based_on_ECG_features_A_scoping_review/29478020）。