URS 数据描述性统计
提供输尿管镜（URS）肾结石患者数据的描述性统计，涵盖BMI、年龄等指标及治疗结局，用于结石清除与并发症分析。
基本信息
资源简介
该数据集为 URS（输尿管镜检查/输尿管肾镜）数据的描述性统计。相关研究关注肾结石：矿物盐在尿路中结晶形成结石，多数可随尿液排出，但部分会阻塞输尿管肾盂连接部或输尿管，导致严重下背痛、血尿、呕吐和排尿疼痛。X 射线或 URS 等影像技术常用于检测肾结石，随后使用冲击波碎石（SWL）或激光 URS 将结石碎成小块。为预测结石清除成功与并发症结局，研究使用 15,126 条 SWL 和 2,116 条 URS 患者记录训练人工神经网络模型；记录包含 BMI、年龄等患者指标以及通过医疗仪器和医护人员获得的治疗结局。由于结局失败样本较少，采用最近邻和 SMOTE 过采样，并使用集成建模降低预测噪声。基于混淆矩阵，SWL 结石清除和并发症平均预测准确率分别为 84.8% 和 95.0%，URS 分别为 89.0% 和 92.2%；基于 AUC，SWL 分别为 74.7% 和 62.9%，URS 分别为 77.2% 和 78.9%。这些模型已集成到 Stone Decision Engine Web 应用中，用于根据患者个体指标向医疗提供者建议最佳干预措施。数据模态为表格/统计，用途是支持肾结石治疗结局预测和临床干预建议。
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载，适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
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暂未开放当前数据集主要来源为 Kaggle 公开托管，完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取（Figshare）
- 源站标题：Descriptive statistics for URS data.
- DOI：10.1371/journal.pone.0301812.t002
- 发布日期：2024-05-02
- 文件数：1 个
- 文件总体积：5.63 KB
- 源站页面：https://plos.figshare.com/articles/dataset/Descriptive_statistics_for_URS_data_/25741250
命令行下载
curl -L -o "Table 2.xls" "https://ndownloader.figshare.com/files/46056212"
文件清单
Table 2.xls（5.63 KB）
文件总体积 5.63 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
Kidney stones form when mineral salts crystallize in the urinary tract. While most stones exit the body in the urine stream, some can block the ureteropelvic junction or ureters, leading to severe lower back pain, blood in the urine, vomiting, and painful urination. Imaging technologies, such as X-rays or ureterorenoscopy (URS), are typically used to detect kidney stones. Subsequently, these stones are fragmented into smaller pieces using shock wave lithotripsy (SWL) or laser URS. Both treatments yield subtly different patient outcomes. To predict successful stone removal and complication outcomes, Artificial Neural Network models were trained on 15,126 SWL and 2,116 URS patient records. These records include patient metrics like Body Mass Index and age, as well as treatment outcomes obtained using various medical instruments and healthcare professionals. Due to the low number of outcome failures in the data (e.g., treatment complications), Nearest Neighbor and Synthetic Minority Oversampling Technique (SMOTE) models were implemented to improve prediction accuracies. To reduce noise in the predictions, ensemble modeling was employed. The average prediction accuracies based on Confusion Matrices for SWL stone removal and treatment complications were 84.8% and 95.0%, respectively, while those for URS were 89.0% and 92.2%, respectively. The average prediction accuracies for SWL based on Area-Under-the-Curve were 74.7% and 62.9%, respectively, while those for URS were 77.2% and 78.9%, respectively. Taken together, the approach yielded moderate to high accurate predictions, regardless of treatment or outcome. These models were incorporated into a Stone Decision Engine web application (http://peteranoble.com/webapps.html) that suggests the best interventions to healthcare providers based on individual patient metrics.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0301812.t002（source: https://plos.figshare.com/articles/dataset/Descriptive_statistics_for_URS_data_/25741250）。
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
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