临床医生观察的伤口愈合问题数据集

数据集获取（Figshare）

• 源站标题：Wound healing problems observed by clinicians.
• DOI：10.1371/journal.pone.0315384.t004
• 发布日期：2024-12-09
• 文件数：1 个
• 文件总体积：9.73 KB
• 源站页面：https://plos.figshare.com/articles/dataset/Wound_healing_problems_observed_by_clinicians_/27995374

命令行下载

curl -L -o "Table 4.xls" "https://ndownloader.figshare.com/files/51067255"

文件清单

• Table 4.xls（9.73 KB）

文件总体积 9.73 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
Surgical patients frequently experience post-operative complications at home. Digital remote monitoring of surgical wounds via image-based systems has emerged as a promising solution for early detection and intervention. However, the increased clinician workload from reviewing patient-submitted images presents a challenge. This study utilises artificial intelligence (AI) to prioritise surgical wound images for clinician review, aiming to efficiently manage workload.
Methods and analysis
Conducted from September 2023 to March 2024, the study phases included compiling a training dataset of 37,974 images, creating a testing set of 3,634 images, developing an AI algorithm using ’You Only Look Once’ models, and conducting prospective tests compared against clinical nurse specialists’ evaluations. The primary objective was to validate the AI’s sensitivity in prioritising wound reviews, alongside assessing intra-rater reliability. Secondary objectives focused on specificity, positive predictive value (PPV), and negative predictive value (NPV) for various wound features.
Results
The AI demonstrated a sensitivity of 89%, exceeding the target of 85% and proving effective in identifying cases requiring priority review. Intra-rater reliability was perfect, achieving 100% consistency in repeated assessments. Observations indicated variations in detecting wound characteristics across different skin tones; sensitivity was notably lower for incisional separation and discolouration in darker skin tones. Specificity remained high overall, with some results favouring darker skin tones. The NPV were similar for both light and dark skin tones. However, the NPV was slightly higher for dark skin tones at 95% (95% CI: 93%-97%) compared to 91% (95% CI: 87%-92%) for light skin tones. Both PPV and NPV varied, especially in identifying sutures or staples, indicating areas needing further refinement to ensure equitable accuracy.
Conclusion
The AI algorithm not only met but surpassed the expected sensitivity for identifying priority cases, showing high reliability. Nonetheless, the disparities in performance across skin tones, especially in recognising certain wound characterist

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0315384.t004（source: https://plos.figshare.com/articles/dataset/Wound_healing_problems_observed_by_clinicians_/27995374）。