基于CT血管造影深度学习自动识别适合取栓的血管闭塞（Table_1）

数据集获取（Figshare）

• 源站标题：Table_1_Automated identification of thrombectomy amenable vessel occlusion on computed tomography angiography using deep learning.docx
• DOI：10.3389/fneur.2024.1442025.s001
• 发布日期：2024-07-25
• 文件数：1 个
• 文件总体积：4.85 MB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Automated_identification_of_thrombectomy_amenable_vessel_occlusion_on_computed_tomography_angiography_using_deep_learning_docx/26368090

命令行下载

curl -L -o "Table_1_Automated identification of thrombectomy amenable vessel occlusion on computed tomography angiography using deep learning.docx" "https://ndownloader.figshare.com/files/47914072"

文件清单

• Table_1_Automated identification of thrombectomy amenable vessel occlusion on computed tomography angiography using deep learning.docx（4.85 MB）

文件总体积 4.85 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

IntroductionWe developed and externally validated a fully automated algorithm using deep learning to detect large vessel occlusion (LVO) in computed tomography angiography (CTA).
MethodA total of 2,045 patients with acute ischemic stroke who underwent CTA were included in the development of our model. We validated the algorithm using two separate external datasets: one with 64 patients (external 1) and another with 313 patients (external 2), with ischemic stroke. In the context of current clinical practice, thrombectomy amenable vessel occlusion (TAVO) was defined as an occlusion in the intracranial internal carotid artery (ICA), or in the M1 or M2 segment of the middle cerebral artery (MCA). We employed the U-Net for vessel segmentation on the maximum intensity projection images, followed by the application of the EfficientNetV2 to predict TAVO. The algorithm’s diagnostic performance was evaluated by calculating the area under the receiver operating characteristics curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).
ResultsThe mean age in the training and validation dataset was 68.7 ± 12.6; 56.3% of participants were men, and 18.0% had TAVO. The algorithm achieved AUC of 0.950 (95% CI, 0.915–0.971) in the internal test. For the external datasets 1 and 2, the AUCs were 0.970 (0.897–0.997) and 0.971 (0.924–0.990), respectively. With a fixed sensitivity of 0.900, the specificities and PPVs for the internal test, external test 1, and external test 2 were 0.891, 0.796, and 0.930, and 0.665, 0.583, and 0.667, respectively. The algorithm demonstrated a sensitivity and specificity of approximately 0.95 in both internal and external datasets, specifically for cases involving intracranial ICA or M1-MCA occlusion. However, the diagnostic performance was somewhat reduced for isolated M2-MCA occlusion; the AUC for the internal and combined external datasets were 0.903 (0.812–0.944) and 0.916 (0.816–0.963), respectively.
ConclusionWe developed and externally validated a fully automated algorithm that identifies TAVO. Further research is needed to evaluate its effectiveness in real-world clinical settings. This validate

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fneur.2024.1442025.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Automated_identification_of_thrombectomy_amenable_vessel_occlusion_on_computed_tomography_angiography_using_deep_learning_docx/26368090）。