即时自体荧光成像用于慢性伤口生物负荷实时取样与治疗指导：首次人体试验结果

数据集获取（Figshare）

• 源站标题：<i>Point-of-Care</i> Autofluorescence Imaging for Real-Time Sampling and Treatment Guidance of Bioburden in Chronic Wounds: <i>First-in-Human Results</i>
• DOI：10.1371/journal.pone.0116623
• 发布日期：2015-03-19
• 文件数：5 个
• 文件总体积：697.15 KB
• 源站页面：https://plos.figshare.com/articles/dataset/_Point_of_Care_Autofluorescence_Imaging_for_Real_Time_Sampling_and_Treatment_Guidance_of_Bioburden_in_Chronic_Wounds_First_in_Human_Results_/1344483

命令行下载

curl -L -o "S1_Fig.jpeg" "https://ndownloader.figshare.com/files/1963028"
curl -L -o "S2_Fig.jpeg" "https://ndownloader.figshare.com/files/1963029"
curl -L -o "S3_Fig.jpeg" "https://ndownloader.figshare.com/files/1963030"
curl -L -o "S1_Protocol.pdf" "https://ndownloader.figshare.com/files/1963031"
curl -L -o "S1_STARD_Checklist.doc" "https://ndownloader.figshare.com/files/1963032"

文件清单

• S1_Fig.jpeg（108.50 KB）
• S2_Fig.jpeg（93.94 KB）
• S3_Fig.jpeg（133.60 KB）
• S1_Protocol.pdf（302.75 KB）
• S1_STARD_Checklist.doc（58.37 KB）

文件总体积 697.15 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

Background
Traditionally, chronic wound infection is diagnosed by visual inspection under white light and microbiological sampling, which are subjective and suboptimal, respectively, thereby delaying diagnosis and treatment. To address this, we developed a novel handheld, fluorescence imaging device (PRODIGI) that enables non-contact, real-time, high-resolution visualization and differentiation of key pathogenic bacteria through their endogenous autofluorescence, as well as connective tissues in wounds.
Methods and Findings
This was a two-part Phase I, single center, non-randomized trial of chronic wound patients (male and female, ≥18 years; UHN REB #09-0015-A for part 1; UHN REB #12-5003 for part 2; clinicaltrials.gov Identifier: NCT01378728 for part 1 and NCT01651845 for part 2). Part 1 (28 patients; 54% diabetic foot ulcers, 46% non-diabetic wounds) established the feasibility of autofluorescence imaging to accurately guide wound sampling, validated against blinded, gold standard swab-based microbiology. Part 2 (12 patients; 83.3% diabetic foot ulcers, 16.7% non-diabetic wounds) established the feasibility of autofluorescence imaging to guide wound treatment and quantitatively assess treatment response. We showed that PRODIGI can be used to guide and improve microbiological sampling and debridement of wounds in situ, enabling diagnosis, treatment guidance and response assessment in patients with chronic wounds. PRODIGI is safe, easy to use and integrates into the clinical workflow. Clinically significant bacterial burden can be detected in seconds, quantitatively tracked over days-to-months and their biodistribution mapped within the wound bed, periphery, and other remote areas.
Conclusions
PRODIGI represents a technological advancement in wound sampling and treatment guidance for clinical wound care at the point-of-care.
Trial Registration
ClinicalTrials.gov NCT01651845; ClinicalTrials.gov NCT01378728

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0116623（source: https://plos.figshare.com/articles/dataset/_Point_of_Care_Autofluorescence_Imaging_for_Real_Time_Sampling_and_Treatment_Guidance_of_Bioburden_in_Chronic_Wounds_First_in_Human_Results_/1344483）。