重症肌无力核心检查中数字工具与人工智能应用表1

数据集获取（Figshare）

• 源站标题：Table 1_Application of digital tools and artificial intelligence in the Myasthenia Gravis Core Examination.docx
• DOI：10.3389/fneur.2024.1474884.s001
• 发布日期：2024-12-04
• 文件数：1 个
• 文件总体积：238.98 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Application_of_digital_tools_and_artificial_intelligence_in_the_Myasthenia_Gravis_Core_Examination_docx/27958158

命令行下载

curl -L -o "Table 1_Application of digital tools and artificial intelligence in the Myasthenia Gravis Core Examination.docx" "https://ndownloader.figshare.com/files/50966553"

文件清单

• Table 1_Application of digital tools and artificial intelligence in the Myasthenia Gravis Core Examination.docx（238.98 KB）

文件总体积 238.98 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

BackgroundAdvances in video image analysis and artificial intelligence provide opportunities to transform how patients are evaluated. In this study, we assessed the ability to quantify Zoom video recordings of a standardized neurological examination— the Myasthenia Gravis Core Examination (MG-CE)—designed for telemedicine evaluations.
MethodsWe used Zoom (Zoom Video Communications) videos of patients with myasthenia gravis (MG) who underwent the MG-CE. Computer vision, in combination with artificial intelligence methods, was used to develop algorithms to analyze the videos, with a focus on eye and body motions. To assess the examinations involving vocalization, signal processing methods, such as natural language processing (NLP), were developed. A series of algorithms were developed to automatically compute the metrics of the MG-CE.
ResultsA total of 51 patients with MG were assessed, with videos recorded twice on separate days, while 15 control subjects were evaluated once. We successfully quantified the positions of the lids, eyes, and arms and developed respiratory metrics based on breath counts. The cheek puff exercise was found to have limited value for quantification. Technical limitations included variations in illumination, bandwidth, and the fact that the recording was conducted from the examiner’s side rather than the patient’s side.
ConclusionSeveral aspects of the MG-CE can be quantified to produce continuous measurements using standard Zoom video recordings. Further development of the technology will enable trained non-physician healthcare providers to conduct precise examinations of patients with MG outside of conventional clinical settings, including for the purpose of clinical trials.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fneur.2024.1474884.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Application_of_digital_tools_and_artificial_intelligence_in_the_Myasthenia_Gravis_Core_Examination_docx/27958158）。