基于深度学习的糖尿病视网膜病变分级模型验证数据表3
包含1089张眼底图像，用于验证糖尿病视网膜病变深度学习分级模型EyeWisdom V1的性能。
基本信息
资源简介
基于深度学习的糖尿病视网膜病变分级模型验证数据表3
【一句话概览】
本数据集是 figshare 公开的影像数据集，收录 1 个文件、合计 106.33 KB，主题涵盖 diabetic retinopathy、artificial intelligence、validation、eye wisdom V1、sensitivity，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《Data_Sheet_3_The Validation of Deep Learning-Based Grading Model for Diabetic Retinopathy.PDF》。配套论文《Prevalence and risk factors for diabetic retinopathy in China: a multi-hospital-based cross-sectional study》发表于 British Journal of Ophthalmology（2017），DOI：10.1136/bjophthalmol-2017-310316。源站首次发布：2022-05-16。
【数据内容】
源站原始描述：PurposeTo evaluate the performance of a deep learning (DL)-based artificial intelligence (AI) hierarchical diagnosis software, EyeWisdom V1 for diabetic retinopathy (DR). Materials and MethodsThe prospective study was a multicenter, double-blind, and self-controlled clinical trial. Non-dilated posterior pole fundus images were evaluated by ophthalmologists and EyeWisdom V1, respectively. The diagnosis of manual grading was considered as the gold standard. Primary evaluation index (sensitivity an…
【数据结构与技术规格】
文件数 1 个，合计 106.33 KB。Data_Sheet_3_The Validation of Deep Learning-Based Grading Model for Diabetic Retinopathy.PDF：application/pdf，106.33 KB。数据集 DOI：10.3389/fmed.2022.839088.s003。
【主题与分类】
源站学科分类：Radiology and Organ Imaging、Foetal Development and Medicine、Obstetrics and Gynaecology、Primary Health Care、Medical and Health Sciences not elsewhere classified、Dermatology。主题标签：diabetic retinopathy、artificial intelligence、validation、eye wisdom V1、sensitivity、specificity。
【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://frontiersin.figshare.com/articles/dataset/Data_Sheet_3_The_Validation_of_Deep_Learning-Based_Grading_Model_for_Diabetic_Retinopathy_PDF/19769383
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Zhang, Wen-fei; Li, Dong-hong; Wei, Qi-jie; Ding, Da-yong; Meng, Li-hui; Wang, Yue-lin; et al. (2022). Data_Sheet_3_The Validation of Deep Learning-Based Grading Model for Diabetic Retinopathy.PDF. Frontiers. Dataset
【使用注意】
· 源站最后修改于 2023-06-01，引用时请注意版本时效。
下载信息
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使用方式
数据集获取（Figshare）
- 源站标题：Data_Sheet_3_The Validation of Deep Learning-Based Grading Model for Diabetic Retinopathy.PDF
- DOI：10.3389/fmed.2022.839088.s003
- 发布日期：2022-05-16
- 文件数：1 个
- 文件总体积：106.33 KB
- 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_3_The_Validation_of_Deep_Learning-Based_Grading_Model_for_Diabetic_Retinopathy_PDF/19769383
命令行下载
curl -L -o "Data_Sheet_3_The Validation of Deep Learning-Based Grading Model for Diabetic Retinopathy.PDF" "https://ndownloader.figshare.com/files/35124106"
文件清单
Data_Sheet_3_The Validation of Deep Learning-Based Grading Model for Diabetic Retinopathy.PDF（106.33 KB）
文件总体积 106.33 KB（源站 API 实测）。
数据说明
PurposeTo evaluate the performance of a deep learning (DL)-based artificial intelligence (AI) hierarchical diagnosis software, EyeWisdom V1 for diabetic retinopathy (DR).
Materials and MethodsThe prospective study was a multicenter, double-blind, and self-controlled clinical trial. Non-dilated posterior pole fundus images were evaluated by ophthalmologists and EyeWisdom V1, respectively. The diagnosis of manual grading was considered as the gold standard. Primary evaluation index (sensitivity and specificity) and secondary evaluation index like positive predictive values (PPV), negative predictive values (NPV), etc., were calculated to evaluate the performance of EyeWisdom V1.
ResultsA total of 1,089 fundus images from 630 patients were included, with a mean age of (56.52 ± 11.13) years. For any DR, the sensitivity, specificity, PPV, and NPV were 98.23% (95% CI 96.93–99.08%), 74.45% (95% CI 69.95-78.60%), 86.38% (95% CI 83.76-88.72%), and 96.23% (95% CI 93.50-98.04%), respectively; For sight-threatening DR (STDR, severe non-proliferative DR or worse), the above indicators were 80.47% (95% CI 75.07-85.14%), 97.96% (95% CI 96.75-98.81%), 92.38% (95% CI 88.07-95.50%), and 94.23% (95% CI 92.46-95.68%); For referral DR (moderate non-proliferative DR or worse), the sensitivity and specificity were 92.96% (95% CI 90.66-94.84%) and 93.32% (95% CI 90.65-95.42%), with the PPV of 94.93% (95% CI 92.89-96.53%) and the NPV of 90.78% (95% CI 87.81-93.22%). The kappa score of EyeWisdom V1 was 0.860 (0.827-0.890) with the AUC of 0.958 for referral DR.
ConclusionThe EyeWisdom V1 could provide reliable DR grading and referral recommendation based on the fundus images of diabetics.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.3389/fmed.2022.839088.s003（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_3_The_Validation_of_Deep_Learning-Based_Grading_Model_for_Diabetic_Retinopathy_PDF/19769383）。
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
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