预测降主动脉直径的机器学习方法（表3）

数据集获取（Figshare）

• 源站标题：Table_3_A machine learning approach for predicting descending thoracic aortic diameter.XLSX
• DOI：10.3389/fcvm.2023.1097116.s006
• 发布日期：2023-02-13
• 文件数：1 个
• 文件总体积：18.08 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_3_A_machine_learning_approach_for_predicting_descending_thoracic_aortic_diameter_XLSX/22085087

命令行下载

curl -L -o "Table_3_A machine learning approach for predicting descending thoracic aortic diameter.XLSX" "https://ndownloader.figshare.com/files/39226760"

文件清单

• Table_3_A machine learning approach for predicting descending thoracic aortic diameter.XLSX（18.08 KB）

文件总体积 18.08 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

BackgroundTo establish models for predicting descending thoracic aortic diameters and provide evidence for selecting the size of the stent graft for TBAD patients.
MethodsA total of 200 candidates without severe deformation of aorta were included. CTA information was collected and 3D reconstructed. In the reconstructed CTA, a total of 12 cross-sections of peripheral vessels were made perpendicular to the axis of flow of the aorta. Parameters of the cross sections and basic clinical characteristics were used for prediction. The data was randomly split into the training set and the test set in an 8:2 ratio. To fully describe diameters of descending thoracic aorta, three predicted points were set based quadrisection, and a total of 12 models at three predicted points were established using four algorithms included linear regression (LR), support vector machine (SVM), Extra-Tree regression (ETR) and random forest regression (RFR). The performance of models was evaluated by mean square error (MSE) of the prediction value, and the ranking of feature importance was given by Shapley value. After modeling, prognosis of five TEVAR cases and stent oversizing were compared.
ResultsWe identified a series of parameters which affect the diameter of descending thoracic aorta, including age, hypertension, the area of proximal edge of superior mesenteric artery, etc. Among four predictive models, all the MSEs of SVM models at three different predicted position were less than 2 mm2, with approximately 90% predicted diameters error less than 2 mm in the test sets. In patients with dSINE, stent oversizing was about 3 mm, while only 1 mm in patients without complications.
ConclusionThe predictive models established by machine learning revealed the relationship between basic characteristics and diameters of different segment of descending aorta, which help to provide evidence for selecting the matching distal size of the stent for TBAD patients, thereby reducing the incidence of TEVAR complications.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fcvm.2023.1097116.s006（source: https://frontiersin.figshare.com/articles/dataset/Table_3_A_machine_learning_approach_for_predicting_descending_thoracic_aortic_diameter_XLSX/22085087）。