胆道闭锁中人工神经网络的诊断价值与有效性数据表1

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_Diagnostic Value and Effectiveness of an Artificial Neural Network in Biliary Atresia.doc
• DOI：10.3389/fped.2020.00409.s001
• 发布日期：2020-08-06
• 文件数：1 个
• 文件总体积：817.71 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Diagnostic_Value_and_Effectiveness_of_an_Artificial_Neural_Network_in_Biliary_Atresia_doc/12769439

命令行下载

curl -L -o "Data_Sheet_1_Diagnostic Value and Effectiveness of an Artificial Neural Network in Biliary Atresia.doc" "https://ndownloader.figshare.com/files/24161552"

文件清单

• Data_Sheet_1_Diagnostic Value and Effectiveness of an Artificial Neural Network in Biliary Atresia.doc（817.71 KB）

文件总体积 817.71 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

Objectives: Biliary atresia (BA) is a devastating pediatric liver disease. Early diagnosis is important for timely intervention and better prognosis. Using clinical parameters for non-invasive and efficient BA diagnosis, we aimed to establish an artificial neural network (ANN).
Methods: A total of 2,384 obstructive jaundice patients from 2012 to 2017 and their 137 clinical parameters were screened for eligibility. A standard binary classification feed-forward ANN was employed. The network was trained and validated for accuracy. Gamma-glutamyl transpeptidase (GGT) level was used as an independent predictor and a comparison to assess the network effectiveness.
Results: We included 46 parameters and 1,452 patients for ANN modeling. Total bilirubin, direct bilirubin, and GGT were the most significant indicators. The network consisted of an input layer, 3 hidden layers with 12 neurons each, and an output layer. The network showed good predictive property with a high area under curve (AUC) (0.967, sensitivity 97.2% and specificity 91.0%). Five-fold cross validation showed the mean accuracy for training data of 93.2% and for validation data of 88.6%.
Conclusions: The high accuracy and efficiency demonstrated by the ANN model is promising in the noninvasive diagnosis of BA and could be considered as in a low-cost and independent expert diagnosis system.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fped.2020.00409.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Diagnostic_Value_and_Effectiveness_of_an_Artificial_Neural_Network_in_Biliary_Atresia_doc/12769439）。