测量计算机视觉人工智能解释HIV自检结果图像性能的研究数据集

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_Measuring the performance of computer vision artificial intelligence to interpret images of HIV self-testing results.docx
• DOI：10.3389/fpubh.2024.1334881.s001
• 发布日期：2024-02-07
• 文件数：1 个
• 文件总体积：33.29 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Measuring_the_performance_of_computer_vision_artificial_intelligence_to_interpret_images_of_HIV_self-testing_results_docx/25167314

命令行下载

curl -L -o "Data_Sheet_1_Measuring the performance of computer vision artificial intelligence to interpret images of HIV self-testing results.docx" "https://ndownloader.figshare.com/files/44451074"

文件清单

• Data_Sheet_1_Measuring the performance of computer vision artificial intelligence to interpret images of HIV self-testing results.docx（33.29 KB）

文件总体积 33.29 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

IntroductionHIV self-testing (HIVST) is highly sensitive and specific, addresses known barriers to HIV testing (such as stigma), and is recommended by the World Health Organization as a testing option for the delivery of HIV pre-exposure prophylaxis (PrEP). Nevertheless, HIVST remains underutilized as a diagnostic tool in community-based, differentiated HIV service delivery models, possibly due to concerns about result misinterpretation, which could lead to inadvertent onward transmission of HIV, delays in antiretroviral therapy (ART) initiation, and incorrect initiation on PrEP. Ensuring that HIVST results are accurately interpreted for correct clinical decisions will be critical to maximizing HIVST’‘s potential. Early evidence from a few small pilot studies suggests that artificial intelligence (AI) computer vision and machine learning could potentially assist with this task. As part of a broader study that task-shifted HIV testing to a new setting and cadre of healthcare provider (pharmaceutical technologists at private pharmacies) in Kenya, we sought to understand how well AI technology performed at interpreting HIVST results.
MethodsAt 20 private pharmacies in Kisumu, Kenya, we offered free blood-based HIVST to clients ≥18 years purchasing products indicative of sexual activity (e.g., condoms). Trained pharmacy providers assisted clients with HIVST (as needed), photographed the completed HIVST, and uploaded the photo to a web-based platform. In real time, each self-test was interpreted independently by the (1) client and (2) pharmacy provider, with the HIVST images subsequently interpreted by (3) an AI algorithm (trained on lab-captured images of HIVST results) and (4) an expert panel of three HIVST readers. Using the expert panel’'s determination as the ground truth, we calculated the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for HIVST result interpretation for the AI algorithm as well as for pharmacy clients and providers, for comparison.
ResultsFrom March to June 2022, we screened 1,691 pharmacy clients and enrolled 1,500 in the study. All clients completed HIVST. Among 854 clients whose HIVST images wer

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fpubh.2024.1334881.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Measuring_the_performance_of_computer_vision_artificial_intelligence_to_interpret_images_of_HIV_self-testing_results_docx/25167314）。