机器学习辅助吞咽评估：基于深度学习的质量改进工具筛查中风后吞咽困难数据表

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_Machine-learning assisted swallowing assessment: a deep learning-based quality improvement tool to screen for post-stroke dysphagia.docx
• DOI：10.3389/fnins.2023.1302132.s001
• 发布日期：2023-11-24
• 文件数：1 个
• 文件总体积：19.18 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Machine-learning_assisted_swallowing_assessment_a_deep_learning-based_quality_improvement_tool_to_screen_for_post-stroke_dysphagia_docx/24629805

命令行下载

curl -L -o "Data_Sheet_1_Machine-learning assisted swallowing assessment: a deep learning-based quality improvement tool to screen for post-stroke dysphagia.docx" "https://ndownloader.figshare.com/files/43275381"

文件清单

• Data_Sheet_1_Machine-learning assisted swallowing assessment: a deep learning-based quality improvement tool to screen for post-stroke dysphagia.docx（19.18 KB）

文件总体积 19.18 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

IntroductionPost-stroke dysphagia is common and associated with significant morbidity and mortality, rendering bedside screening of significant clinical importance. Using voice as a biomarker coupled with deep learning has the potential to improve patient access to screening and mitigate the subjectivity associated with detecting voice change, a component of several validated screening protocols.
MethodsIn this single-center study, we developed a proof-of-concept model for automated dysphagia screening and evaluated the performance of this model on training and testing cohorts. Patients were admitted to a comprehensive stroke center, where primary English speakers could follow commands without significant aphasia and participated on a rolling basis. The primary outcome was classification either as a pass or fail equivalent using a dysphagia screening test as a label. Voice data was recorded from patients who spoke a standardized set of vowels, words, and sentences from the National Institute of Health Stroke Scale. Seventy patients were recruited and 68 were included in the analysis, with 40 in training and 28 in testing cohorts, respectively. Speech from patients was segmented into 1,579 audio clips, from which 6,655 Mel-spectrogram images were computed and used as inputs for deep-learning models (DenseNet and ConvNext, separately and together). Clip-level and participant-level swallowing status predictions were obtained through a voting method.
ResultsThe models demonstrated clip-level dysphagia screening sensitivity of 71% and specificity of 77% (F1 = 0.73, AUC = 0.80 [95% CI: 0.78–0.82]). At the participant level, the sensitivity and specificity were 89 and 79%, respectively (F1 = 0.81, AUC = 0.91 [95% CI: 0.77–1.05]).
DiscussionThis study is the first to demonstrate the feasibility of applying deep learning to classify vocalizations to detect post-stroke dysphagia. Our findings suggest potential for enhancing dysphagia screening in clinical settings. https://github.com/UofTNeurology/masa-opensource

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fnins.2023.1302132.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Machine-learning_assisted_swallowing_assessment_a_deep_learning-based_quality_improvement_tool_to_screen_for_post-stroke_dysphagia_docx/24629805）。