人工智能用于动脉导管未闭——系统综述的Table 1.xlsx

数据集获取（Figshare）

• 源站标题：Table 1_Artificial intelligence for patent ductus arteriosus—a systematic review.xlsx
• DOI：10.3389/fped.2026.1648943
• 发布日期：2026-01-30
• 文件数：1 个
• 文件总体积：40.59 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Artificial_intelligence_for_patent_ductus_arteriosus_a_systematic_review_xlsx/31201885

命令行下载

curl -L -o "Table 1_Artificial intelligence for patent ductus arteriosus—a systematic review.xlsx" "https://ndownloader.figshare.com/files/61495357"

文件清单

• Table 1_Artificial intelligence for patent ductus arteriosus—a systematic review.xlsx（40.59 KB）

文件总体积 40.59 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

IntroductionOptimal management of patent ductus arteriosus (PDA) remains controversial. Complexity in severity appraisal, high-dimensional data, and the need for longitudinal, individualized assessment make PDA a compelling candidate for Artificial Intelligence (AI)-driven approaches. This systematic review evaluates AI research in the context of PDA, identifying strengths, limitations, and future directions.
MethodsFollowing PRISMA 2020, databases were searched for peer-reviewed articles from January 1, 2010, to May 31, 2025. Eleven studies met inclusion criteria. Data on design, population, sources, AI methods, performance, validation, limitations, and explainability were extracted. Risk of bias was assessed using the Prediction model Risk of Bias Assessment Tool and Joanna Briggs Institute Critical Appraisal Checklist; reporting quality using the Minimum Information about Clinical AI Modeling checklist. Heterogeneity precluded meta-analysis; therefore findings were synthesized narratively.
ResultsEleven studies addressed diagnosis/screening (n = 5), treatment-response prediction (n = 2), risk-factor identification (n = 2), treatment-complication prediction (n = 1), and subphenotype analysis (n = 1). Ten were retrospective; nine single-center, one multi-center, and one used a national registry. Sample sizes were mostly Artificial intelligence shows feasibility for supporting PDA risk stratification, diagnosis, severity assessment, and prediction of treatment-related outcomes. However, current applications remain in early, pilot-stage development and are not yet suitable for clinical implementation. Future work should prioritize clinically meaningful tasks, scientifically rigorous and bias-aware methodologies, larger and more representative cohorts, and systematic external validation. Fairness, explainability, and reproducibility must be addressed to support translation. Continued methodological refinement and clinical grounding will be key to unlocking the potential of these technologies for this highly vulnerable patient population in the future.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fped.2026.1648943（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Artificial_intelligence_for_patent_ductus_arteriosus_a_systematic_review_xlsx/31201885）。