基于神经网络的ESWL治疗后无结石状态预测算法数据集

数据集获取（Figshare）

• 源站标题：A neural network - based algorithm for predicting stone -free status after ESWL therapy
• DOI：10.6084/m9.figshare.5772477.v1
• 发布日期：2018-01-10
• 文件数：3 个
• 文件总体积：292.69 KB
• 源站页面：https://scielo.figshare.com/articles/dataset/A_neural_network_-_based_algorithm_for_predicting_stone_-free_status_after_ESWL_therapy/5772477

命令行下载

curl -L -o "1677-5538-ibju-43-06-1110-gf01.jpg" "https://ndownloader.figshare.com/files/10180044"
curl -L -o "Table_1.xls" "https://ndownloader.figshare.com/files/10180062"
curl -L -o "Table_2.xls" "https://ndownloader.figshare.com/files/10180071"

文件清单

• 1677-5538-ibju-43-06-1110-gf01.jpg（281.43 KB）
• Table_1.xls（5.63 KB）
• Table_2.xls（5.63 KB）

文件总体积 292.69 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

ABSTRACT Objective: The prototype artificial neural network (ANN) model was developed using data from patients with renal stone, in order to predict stone-free status and to help in planning treatment with Extracorporeal Shock Wave Lithotripsy (ESWL) for kidney stones. Materials and Methods: Data were collected from the 203 patients including gender, single or multiple nature of the stone, location of the stone, infundibulopelvic angle primary or secondary nature of the stone, status of hydronephrosis, stone size after ESWL, age, size, skin to stone distance, stone density and creatinine, for eleven variables. Regression analysis and the ANN method were applied to predict treatment success using the same series of data. Results: Subsequently, patients were divided into three groups by neural network software, in order to implement the ANN: training group (n=139), validation group (n=32), and the test group (n=32). ANN analysis demonstrated that the prediction accuracy of the stone-free rate was 99.25% in the training group, 85.48% in the validation group, and 88.70% in the test group. Conclusions: Successful results were obtained to predict the stone-free rate, with the help of the ANN model designed by using a series of data collected from real patients in whom ESWL was implemented to help in planning treatment for kidney stones.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.5772477.v1（source: https://scielo.figshare.com/articles/dataset/A_neural_network_-_based_algorithm_for_predicting_stone_-free_status_after_ESWL_therapy/5772477）。