人工智能在血友病临床管理与研究中的影像视角

数据集获取（Figshare）

• 源站标题：Applications of artificial intelligence in clinical management, research, and health administration: imaging perspectives with a focus on hemophilia
• DOI：10.6084/m9.figshare.22586859.v1
• 发布日期：2023-04-11
• 文件数：2 个
• 文件总体积：64.84 MB
• 源站页面：https://tandf.figshare.com/articles/dataset/Applications_of_artificial_intelligence_in_clinical_management_research_and_health_administration_imaging_perspectives_with_a_focus_on_hemophilia/22586859

命令行下载

curl -L -o "ierr_a_2192474_sm4712.tif" "https://ndownloader.figshare.com/files/40098069"
curl -L -o "ierr_a_2192474_sm4545.tif" "https://ndownloader.figshare.com/files/40098072"

文件清单

• ierr_a_2192474_sm4712.tif（34.20 MB）
• ierr_a_2192474_sm4545.tif（30.64 MB）

文件总体积 64.84 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

Joints of persons with hemophilia are frequently affected by repetitive hemarthrosis. In this paper, concepts, perks, and quirks of the use of artificial intelligence (AI), machine learning (ML), and deep learning are reviewed within clinical and research contexts of hemophilia and other blood-induced disorders’ patient care, targeted to the imaging diagnosis of hemophilic joints, under the perspective of different stakeholders (radiologists, hematologists, nurses, physiotherapists, technologists, researchers, managers, and patients/caregivers).
Rubrics that determine the suitability of the utilization of AI in blood-induced disorders’ patient care, including diagnosis and follow-up of patients are discussed, focusing on features in which AI can replace or augment the role of radiology in the clinical management and in research of patients. Insights on features in the design and conduct of AI projects in which the human intervention remains critical are provided.
The author discusses research concepts in radiogenomics, and challenges for the utilization of AI in different healthcare fields such as patient safety, data sharing and privacy regulations, workforce education and future jobs’ shortage. Finally, the author proposes alternatives and potential solutions to mitigate challenges in successfully deploying ML algorithms into clinical practice.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.22586859.v1（source: https://tandf.figshare.com/articles/dataset/Applications_of_artificial_intelligence_in_clinical_management_research_and_health_administration_imaging_perspectives_with_a_focus_on_hemophilia/22586859）。