基于单视野眼底照相的糖尿病视网膜病变转诊算法的开发与验证

基于单视野眼底照片的糖尿病视网膜病变转诊算法开发与验证数据集,含99人对50张DR图像的评估结果。

NIAID Data Ecosystem
浏览 19
医学影像6 months criteriaoptometristreview1 yearsingle-field 45 degreeSingle-Field Fundus Photography PurposeDiabetic Retinopathy Referral AlgorithmimageretinopathyAUC99 participantsreferral
Development and Validation of a Diabetic Retinopathy Referral Algorithm Based on Single-Field Fundus Photography

基本信息

模态
医学影像
大小
0.000328458 GB
许可
CC BY 4.0
创建/更新时间
2026-10-07
版本
v1

资源简介

基于单视野眼底照相的糖尿病视网膜病变转诊算法的开发与验证

【一句话概览】

本数据集是 figshare 公开的影像数据集,收录 2 个文件、合计 328.46 KB,主题涵盖 6 months criteria、optometrist、review、1 year、single-field 45 degree,可用于医学图像分割、检测与分类模型的训练与评测,CC BY 4.0 许可开放获取。

【数据集背景】

本数据集源站标题为《Development and Validation of a Diabetic Retinopathy Referral Algorithm Based on Single-Field Fundus Photography》。配套论文《Development and Validation of a Diabetic Retinopathy Referral Algorithm Based on Single-Field Fundus Photography》发表于 PLOS ONE(2016),DOI:10.1371/journal.pone.0163108。源站首次发布:2016-09-23。

【数据内容】

源站原始描述:Purpose To develop a simplified algorithm to identify and refer diabetic retinopathy (DR) from single-field retinal images specifically for sight-threatening diabetic retinopathy for appropriate care (ii) to determine the agreement and diagnostic accuracy of the algorithm as a pilot study among optometrists versus “gold standard” (retinal specialist grading). Methods The severity of DR was scored based on colour photo using a colour coded algorithm, which included the lesions of DR and number of…

【数据结构与技术规格】

文件数 2 个,合计 328.46 KB。S1 Dataset.XLSX:application/vnd.openxmlformats-officedocument.spreadsheetml.sheet,206.43 KB。S1 Table.DOCX:application/vnd.openxmlformats-officedocument.wordprocessingml.document,122.03 KB。数据集 DOI:10.1371/journal.pone.0163108。

【主题与分类】

源站学科分类:Medicine、Cell Biology、Biotechnology、Developmental Biology、Science Policy、Infectious Diseases。主题标签:6 months criteria、optometrist、review、1 year、single-field 45 degree、Single-Field Fundus Photography Purpose、Diabetic Retinopathy Referral Algorithm、image、retinopathy、AUC。

【适用方向】

影像类数据可用于医学图像分析模型(检测、分割、分类)的训练与评测,也可用于标注质量与成像参数相关性的研究。

【获取与许可】

源站页面:https://plos.figshare.com/articles/dataset/Development_and_Validation_of_a_Diabetic_Retinopathy_Referral_Algorithm_Based_on_Single-Field_Fundus_Photography/3857586

使用许可:CC BY 4.0(https://creativecommons.org/licenses/by/4.0/)。

规范引用:Srinivasan, Sangeetha; Shetty, Sharan; Natarajan, Viswanathan; Sharma, Tarun; Raman, Rajiv (2016). Development and Validation of a Diabetic Retinopathy Referral Algorithm Based on Single-Field Fundus Photography. PLOS ONE. Dataset

【使用注意】

· 源站最后修改于 2023-05-31,引用时请注意版本时效。

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下载信息

注册下载

需要注册 Kaggle 账号并登录后下载,适合需要跟踪下载记录和使用 API 的用户。

暂未开放

公开下载

无需注册即可直接获取公开样本或文档,适合快速预览和评估数据集质量。

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公开数据集受托下载与技术交付服务。

提供高速下载与技术交付服务(收技术服务费,非数据销售)

暂未开放

当前数据集主要来源为 Kaggle 公开托管,完整影像包建议通过原始链接或 Kaggle API 获取。

使用方式

数据集获取(Figshare)

命令行下载

curl -L -o "S1 Dataset.XLSX" "https://ndownloader.figshare.com/files/6044058"
curl -L -o "S1 Table.DOCX" "https://ndownloader.figshare.com/files/6044070"

文件清单

  • S1 Dataset.XLSX(206.43 KB)
  • S1 Table.DOCX(122.03 KB)

文件总体积 328.46 KB(源站 API 实测)。

数据说明

Purpose
To develop a simplified algorithm to identify and refer diabetic retinopathy (DR) from single-field retinal images specifically for sight-threatening diabetic retinopathy for appropriate care (ii) to determine the agreement and diagnostic accuracy of the algorithm as a pilot study among optometrists versus “gold standard” (retinal specialist grading).
Methods
The severity of DR was scored based on colour photo using a colour coded algorithm, which included the lesions of DR and number of quadrants involved. A total of 99 participants underwent training followed by evaluation. Data of the 99 participants were analyzed. Fifty posterior pole 45 degree retinal images with all stages of DR were presented. Kappa scores (κ), areas under the receiver operating characteristic curves (AUCs), sensitivity and specificity were determined, with further comparison between working optometrists and optometry students.
Results
Mean age of the participants was 22 years (range: 19–43 years), 87% being women. Participants correctly identified 91.5% images that required immediate referral (κ) = 0.696), 62.5% of images as requiring review after 6 months (κ = 0.462), and 51.2% of those requiring review after 1 year (κ = 0.532). The sensitivity and specificity of the optometrists were 91% and 78% for immediate referral, 62% and 84% for review after 6 months, and 51% and 95% for review after 1 year, respectively. The AUC was the highest (0.855) for immediate referral, second highest (0.824) for review after 1 year, and 0.727 for review after 6 months criteria. Optometry students performed better than the working optometrists for all grades of referral.
Conclusions
The diabetic retinopathy algorithm assessed in this work is a simple and a fairly accurate method for appropriate referral based on single-field 45 degree posterior pole retinal images.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0163108(source: https://plos.figshare.com/articles/dataset/Development_and_Validation_of_a_Diabetic_Retinopathy_Referral_Algorithm_Based_on_Single-Field_Fundus_Photography/3857586)。

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