膝关节骨关节炎风险预测的简易评分系统与人工神经网络横断面研究

数据集获取（Figshare）

• 源站标题：Simple Scoring System and Artificial Neural Network for Knee Osteoarthritis Risk Prediction: A Cross-Sectional Study
• DOI：10.1371/journal.pone.0148724
• 发布日期：2016-02-09
• 文件数：2 个
• 文件总体积：180.74 KB
• 源站页面：https://plos.figshare.com/articles/dataset/_Simple_Scoring_System_and_Artificial_Neural_Network_for_Knee_Osteoarthritis_Risk_Prediction_A_Cross_Sectional_Study_/1643387

命令行下载

curl -L -o "S1_Table.doc" "https://ndownloader.figshare.com/files/2635794"
curl -L -o "S2_Table.doc" "https://ndownloader.figshare.com/files/2635795"

文件清单

• S1_Table.doc（143.87 KB）
• S2_Table.doc（36.86 KB）

文件总体积 180.74 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

Background
Knee osteoarthritis (OA) is the most common joint disease of adults worldwide. Since the treatments for advanced radiographic knee OA are limited, clinicians face a significant challenge of identifying patients who are at high risk of OA in a timely and appropriate way. Therefore, we developed a simple self-assessment scoring system and an improved artificial neural network (ANN) model for knee OA.
Methods
The Fifth Korea National Health and Nutrition Examination Surveys (KNHANES V-1) data were used to develop a scoring system and ANN for radiographic knee OA. A logistic regression analysis was used to determine the predictors of the scoring system. The ANN was constructed using 1777 participants and validated internally on 888 participants in the KNHANES V-1. The predictors of the scoring system were selected as the inputs of the ANN. External validation was performed using 4731 participants in the Osteoarthritis Initiative (OAI). Area under the curve (AUC) of the receiver operating characteristic was calculated to compare the prediction models.
Results
The scoring system and ANN were built using the independent predictors including sex, age, body mass index, educational status, hypertension, moderate physical activity, and knee pain. In the internal validation, both scoring system and ANN predicted radiographic knee OA (AUC 0.73 versus 0.81, ppppConclusions
The self-assessment scoring system may be useful for identifying the adults at high risk for knee OA. The performance of the scoring system is improved significantly by the ANN. We provided an ANN calculator to simply predict the knee OA risk.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0148724（source: https://plos.figshare.com/articles/dataset/_Simple_Scoring_System_and_Artificial_Neural_Network_for_Knee_Osteoarthritis_Risk_Prediction_A_Cross_Sectional_Study_/1643387）。