人工智能心电图预测冠状动脉钙化数据集

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_Artificial Intelligence-Enabled ECG Algorithm for the Prediction of Coronary Artery Calcification.DOCX
• DOI：10.3389/fcvm.2022.849223.s001
• 发布日期：2022-04-06
• 文件数：1 个
• 文件总体积：1.38 MB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Artificial_Intelligence-Enabled_ECG_Algorithm_for_the_Prediction_of_Coronary_Artery_Calcification_DOCX/19523026

命令行下载

curl -L -o "Data_Sheet_1_Artificial Intelligence-Enabled ECG Algorithm for the Prediction of Coronary Artery Calcification.DOCX" "https://ndownloader.figshare.com/files/34703149"

文件清单

• Data_Sheet_1_Artificial Intelligence-Enabled ECG Algorithm for the Prediction of Coronary Artery Calcification.DOCX（1.38 MB）

文件总体积 1.38 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

Coronary artery calcium (CAC), which can be measured in various types of computed tomography (CT) examinations, is a hallmark of coronary artery atherosclerosis. However, despite the clinical value of CAC scores in predicting cardiovascular events, routine measurement of CAC scores is limited due to high cost, radiation exposure, and lack of widespread availability. It would be of great clinical significance if CAC could be predicted by electrocardiograms (ECGs), which are cost-effective and routinely performed during various medical checkups. We aimed to develop binary classification artificial intelligence (AI) models that predict CAC using only ECGs as input. Moreover, we aimed to address the generalizability of our model in different environments by externally validating our model on a dataset from a different institution. Among adult patients, standard 12-lead ECGs were extracted if measured within 60 days before or after the CAC scores, and labeled with the corresponding CAC scores. We constructed deep convolutional neural network models based on residual networks using only the raw waveforms of the ECGs as input, predicting CAC at different levels, namely CAC score ≥100, ≥400 and ≥1,000. Our AI models performed well in predicting CAC in the training and internal validation dataset [area under the receiver operating characteristics curve (AUROC) 0.753 ± 0.009, 0.802 ± 0.027, and 0.835 ± 0.024 for the CAC score ≥100, ≥400, and ≥1,000 model, respectively]. Our models also performed well in the external validation dataset (AUROC 0.718, 0.777 and 0.803 for the CAC score ≥100, ≥400, and ≥1,000 model, respectively), indicating that our model can generalize well to different but plausibly related populations. Model performance in terms of AUROC increased in the order of CAC score ≥100, ≥400, and ≥1,000 model, indicating that higher CAC scores might be associated with more prominent structural changes of the heart detected by the model. With our AI models, a substantial proportion of previously unrecognized CAC can be afforded with a risk stratification of CAC, enabling initiation of prophylactic therapy, and reducing the adverse consequences related to ischemic h

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fcvm.2022.849223.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Artificial_Intelligence-Enabled_ECG_Algorithm_for_the_Prediction_of_Coronary_Artery_Calcification_DOCX/19523026）。