预测房颤消融后复发：基于左心房曲线M型斑点追踪图像的深度CNN方法数据集

数据集获取（Figshare）

• 源站标题：Table_1_A Novel Approach for Predicting Atrial Fibrillation Recurrence After Ablation Using Deep Convolutional Neural Networks by Assessing Left Atrial Curved M-Mode Speckle-Tracking Images.docx
• DOI：10.3389/fcvm.2020.605642.s001
• 发布日期：2021-01-22
• 文件数：1 个
• 文件总体积：15.82 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_A_Novel_Approach_for_Predicting_Atrial_Fibrillation_Recurrence_After_Ablation_Using_Deep_Convolutional_Neural_Networks_by_Assessing_Left_Atrial_Curved_M-Mode_Speckle-Tracking_Images_docx/13624610

命令行下载

curl -L -o "Table_1_A Novel Approach for Predicting Atrial Fibrillation Recurrence After Ablation Using Deep Convolutional Neural Networks by Assessing Left Atria.docx" "https://ndownloader.figshare.com/files/26148263"

文件清单

• Table_1_A Novel Approach for Predicting Atrial Fibrillation Recurrence After Ablation Using Deep Convolutional Neural Networks by Assessing Left Atria.docx（15.82 KB）

文件总体积 15.82 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

Aims: Curved M-mode images of global strain (GS) and strain rate (GSR) provide sufficiently detailed spatiotemporal information of deformation mechanics. This study investigated whether a deep convolutional neural network (CNN) could accurately classify these images in patients with atrial fibrillation (AF) who underwent radiofrequency catheter ablation (RFCA) with different outcomes.
Methods and Results: We retrospectively evaluated 606 consecutive patients who underwent RFCA for drug-refractory AF. Patients were divided into AF-free (n = 443) and AF-recurrent (n = 163) groups. Transthoracic echocardiography was performed within 24 h after RFCA. Left atrial curved M-mode speckle-tracking images were acquired from randomly selected 163 patients in AF-free group and 163 patients in AF-recurrent group as the dataset for deep CNN modeling. We used the ReLu activation function and repeatedly performed CNN model for 32 times to evaluate the stability of hyperparameters. Logistic regression models with the left atrial dimension, emptying fraction, and peak systolic GS as predictor variables were used for comparisons. Images from the apical 2-chamber (2-C) and 4-chamber (4-C) views had distinct features, leading to different CNN performance between settings; of them, the “4-C GS+4-C GSR” setting provided the highest performance index values. All four predictor variables used for logistic regression modeling were significant; however, none of them, individually or in any combined form, could outperform the optimal CNN model.
Conclusion: The novel approach using deep CNNs for learning features of left atrial curved M-mode speckle-tracking images seems to be optimal for classifying outcome status after AF ablation.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fcvm.2020.605642.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_A_Novel_Approach_for_Predicting_Atrial_Fibrillation_Recurrence_After_Ablation_Using_Deep_Convolutional_Neural_Networks_by_Assessing_Left_Atrial_Curved_M-Mode_Speckle-Tracking_Images_docx/13624610）。