人工智能在输尿管软镜术后脓毒症风险预测中的应用：一项系统综述

数据集获取（Figshare）

• 源站标题：Use of artificial intelligence for sepsis risk prediction after flexible ureteroscopy: a systematic review
• DOI：10.6084/m9.figshare.23659372.v1
• 发布日期：2023-07-11
• 文件数：6 个
• 文件总体积：1.78 MB
• 源站页面：https://scielo.figshare.com/articles/dataset/Use_of_artificial_intelligence_for_sepsis_risk_prediction_after_flexible_ureteroscopy_a_systematic_review/23659372

命令行下载

curl -L -o "b41347a1c6588f855e694bf97b4ad43ce5ea9abb.tif" "https://ndownloader.figshare.com/files/41519317"
curl -L -o "d38fea633da453d4879a10af1a283f7783f9064f.png" "https://ndownloader.figshare.com/files/41519320"
curl -L -o "3c549bbb35939d29bfa5c27ccb5da81217752693.jpg" "https://ndownloader.figshare.com/files/41519323"
curl -L -o "18a63e716ab7a127bb763efef7b3fc9ecf77fa89.tif" "https://ndownloader.figshare.com/files/41519326"
curl -L -o "cfc3c826f87b75185742bf5033949dea0474ace7.png" "https://ndownloader.figshare.com/files/41519329"
curl -L -o "f9ed29027e3e35ef1f1b35e8372da261c3ce7168.jpg" "https://ndownloader.figshare.com/files/41519332"

文件清单

• b41347a1c6588f855e694bf97b4ad43ce5ea9abb.tif（792.26 KB）
• d38fea633da453d4879a10af1a283f7783f9064f.png（68.50 KB）
• 3c549bbb35939d29bfa5c27ccb5da81217752693.jpg（4.91 KB）
• 18a63e716ab7a127bb763efef7b3fc9ecf77fa89.tif（821.31 KB）
• cfc3c826f87b75185742bf5033949dea0474ace7.png（86.75 KB）
• f9ed29027e3e35ef1f1b35e8372da261c3ce7168.jpg（4.95 KB）

文件总体积 1.78 MB（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 Introduction: flexible ureteroscopy is a minimally invasive surgical technique used for the treatment of renal lithiasis. Postoperative urosepsis is a rare but potentially fatal complication. Traditional models used to predict the risk of this condition have limited accuracy, while models based on artificial intelligence are more promising. The objective of this study is to carry out a systematic review regarding the use of artificial intelligence to detect the risk of sepsis in patients with renal lithiasis undergoing flexible ureteroscopy. Methods: the literature review is in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). The keyword search was performed in MEDLINE, Embase, Web of Science and Scopus and resulted in a total of 2,496 articles, of which 2 met the inclusion criteria. Results: both studies used artificial intelligence models to predict the risk of sepsis after flexible uteroscopy. The first had a sample of 114 patients and was based on clinical and laboratory parameters. The second had an initial sample of 132 patients and was based on preoperative computed tomography images. Both obtained good measurements of Area Under the Curve (AUC), sensitivity and specificity, demonstrating good performance. Conclusion: artificial intelligence provides multiple effective strategies for sepsis risk stratification in patients undergoing urological procedures for renal lithiasis, although further studies are needed.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.23659372.v1（source: https://scielo.figshare.com/articles/dataset/Use_of_artificial_intelligence_for_sepsis_risk_prediction_after_flexible_ureteroscopy_a_systematic_review/23659372）。