大血管闭塞性缺血性卒中血栓切除术前预后预测的机器学习系统综述和元分析数据表

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_Pre-thrombectomy prognostic prediction of large-vessel ischemic stroke using machine learning: A systematic review and meta-analysis.docx
• DOI：10.3389/fneur.2022.945813.s001
• 发布日期：2022-09-08
• 文件数：1 个
• 文件总体积：181.96 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Pre-thrombectomy_prognostic_prediction_of_large-vessel_ischemic_stroke_using_machine_learning_A_systematic_review_and_meta-analysis_docx/21065551

命令行下载

curl -L -o "Data_Sheet_1_Pre-thrombectomy prognostic prediction of large-vessel ischemic stroke using machine learning: A systematic review and meta-analysis.docx" "https://ndownloader.figshare.com/files/37376176"

文件清单

• Data_Sheet_1_Pre-thrombectomy prognostic prediction of large-vessel ischemic stroke using machine learning: A systematic review and meta-analysis.docx（181.96 KB）

文件总体积 181.96 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

IntroductionMachine learning (ML) methods are being increasingly applied to prognostic prediction for stroke patients with large vessel occlusion (LVO) treated with endovascular thrombectomy. This systematic review aims to summarize ML-based pre-thrombectomy prognostic models for LVO stroke and identify key research gaps.
MethodsLiterature searches were performed in Embase, PubMed, Web of Science, and Scopus. Meta-analyses of the area under the receiver operating characteristic curves (AUCs) of ML models were conducted to synthesize model performance.
ResultsSixteen studies describing 19 models were eligible. The predicted outcomes include functional outcome at 90 days, successful reperfusion, and hemorrhagic transformation. Functional outcome was analyzed by 10 conventional ML models (pooled AUC=0.81, 95% confidence interval [CI]: 0.77–0.85, AUC range: 0.68–0.93) and four deep learning (DL) models (pooled AUC=0.75, 95% CI: 0.70–0.81, AUC range: 0.71–0.81). Successful reperfusion was analyzed by three conventional ML models (pooled AUC=0.72, 95% CI: 0.56–0.88, AUC range: 0.55–0.88) and one DL model (AUC=0.65, 95% CI: 0.62–0.68).
ConclusionsConventional ML and DL models have shown variable performance in predicting post-treatment outcomes of LVO without generally demonstrating superiority compared to existing prognostic scores. Most models were developed using small datasets, lacked solid external validation, and at high risk of potential bias. There is considerable scope to improve study design and model performance. The application of ML and DL methods to improve the prediction of prognosis in LVO stroke, while promising, remains nascent.
Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021266524, identifier CRD42021266524

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fneur.2022.945813.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Pre-thrombectomy_prognostic_prediction_of_large-vessel_ischemic_stroke_using_machine_learning_A_systematic_review_and_meta-analysis_docx/21065551）。