患者人口统计学与严重程度数据

数据集获取（Figshare）

• 源站标题：Patient demographics and severity.
• DOI：10.1371/journal.pone.0319577.t001
• 发布日期：2025-03-25
• 文件数：1 个
• 文件总体积：5.63 KB
• 源站页面：https://plos.figshare.com/articles/dataset/Patient_demographics_and_severity_/28663730

命令行下载

curl -L -o "Table 1.xls" "https://ndownloader.figshare.com/files/53228480"

文件清单

• Table 1.xls（5.63 KB）

文件总体积 5.63 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

Introduction
Ptosis surgery outcomes are measured by one-dimensional metrics like Marginal Reflex Distance (MRD) and Palpebral Fissure Height (PFH) using ImageJ. However, these methods are insufficient to capture the full range of changes post-surgery. Eyeball Exposure Rate (EER) offers a more comprehensive two-dimensional perspective as metric. This study compares AI-based EER measurements with conventional ImageJ methods for assessing outcome of ptosis surgery. Methods: Images from 50 patients (total 100 eyes) taken before and after surgery were analyzed using manual ImageJ and the AI-tool “Anigma-View”. Statistical tests assessed the accuracy and consistency of both methods, using intraclass correlation coefficients (ICCs) and Bland-Altman plots for comparison.
Results
EER measured by the AI-tool at pre- and post-operation were 58.85% and 75.36%, respectively. Similarly, manual measurements using ImageJ showed an increase from 58.22% to 75.27%. The Intraclass Correlation Coefficients (ICCs) between the AI-tool and manual measurements ranged from 0.984 to 0.994, indicating excellent agreement, with the repeated AI-tool demonstrating high reproducibility (ICC =  1). Bland-Altman plots showed excellent agreement between the two methods and reproducibility of AI-based measurements. Additionally, EER improvement was more prominent in the moderate to severe ptosis group with a 45.94% increase, compared to the mild group with 14.39% increase.
Discussion
The findings revealed no significant differences between AI-tool and manual methods, suggesting that AI-tool is just as reliable. AI-tool to automate measurements offers efficiency and objectivity, making it a valuable method in clinical fields.
Conclusion
AI-based EER analysis is accurate and efficient, providing comparable results to manual methods. Its ability to simplify surgical outcome assessments makes it a promising addition to clinical practice. Further exploration of AI in evaluating three-dimensional changes in ptosis surgery could enhance future surgical assessments and outcomes.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0319577.t001（source: https://plos.figshare.com/articles/dataset/Patient_demographics_and_severity_/28663730）。