Evidence map›Paper›PMID 41922360›Full record

ArticleScientific data2026

A fundus image dataset for intelligent diabetic retinopathy system.

Shaojuan Peng, Shuo Yang, Xinyu Zhao, Yongtao Zhang, Qingjie Bai, Duo Yuan, Yaling Liu, Yarou Hu, Yi Chen, Kaixuan Cui and 5 more

Abstract readDataset
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Shaojuan PengShenzhen Eye Hospital, Shenzhen Eye Center, Southern Medical University, Guangzhou, China.
Shuo YangShenzhen Eye Hospital, Shenzhen Eye Center, Southern Medical University, Guangzhou, China.
Xinyu ZhaoShenzhen Eye Hospital, Shenzhen Eye Center, Southern Medical University, Guangzhou, China.
Yongtao ZhangNational-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, College of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China.
Qingjie BaiThe First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.
Duo YuanShenzhen Eye Hospital, Shenzhen Eye Center, Southern Medical University, Guangzhou, China.
Yaling LiuShenzhen Eye Hospital, Shenzhen Eye Center, Southern Medical University, Guangzhou, China.
Yarou HuShenzhen Eye Hospital, Shenzhen Eye Center, Southern Medical University, Guangzhou, China.
Yi ChenShenzhen Eye Hospital, Shenzhen Eye Center, Southern Medical University, Guangzhou, China.
Kaixuan CuiShenzhen Eye Hospital, Shenzhen Eye Center, Southern Medical University, Guangzhou, China.
Zhen YuShenzhen Eye Hospital, Shenzhen Eye Center, Southern Medical University, Guangzhou, China.
Zhenquan WuShenzhen Eye Hospital, Shenzhen Eye Center, Southern Medical University, Guangzhou, China.
Ruyin TianShenzhen Eye Hospital, Shenzhen Eye Center, Southern Medical University, Guangzhou, China. tianruyin@sz-eyes.com.
Baiying LeiNational-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, College of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China. leiby@szu.edu.cn.ORCID http://orcid.org/0000-0002-3087-2550
Guoming ZhangShenzhen Eye Hospital, Shenzhen Eye Center, Southern Medical University, Guangzhou, China. zhangguoming@sz-eyes.com.

Funding

National Natural Science Foundation of China (National Science Foundation of China) +86-0755-23959600
6 · The paper itself

Abstract

Diabetic retinopathy (DR), the most prevalent microvascular complication of diabetes mellitus, is the leading cause of irreversible vision loss in the global working-age population. At present, deep learning-integrated ultra-wide-field (UWF) image analysis systems have improved DR grading consistency and reduced peripheral lesion misdiagnosis rates, thereby overcoming the limitations of traditional 45° viewing field AI models in detecting peripheral retinal lesions. However, the lack of standardized, high-quality, and publicly available UWF-DR datasets has severely restricted the generalization ability and reliability of AI models in clinical practice. To address this, this study constructed a dataset comprising 1,630 UWF fundus images from 809 patients, which were annotated and classified by three senior ophthalmologists, for development and validation of AI system in UWF-based DR diagnosis. This dataset aims to empower researchers to train more efficient and accurate AI-assisted DR diagnosis systems based on UWF images, advancing its widespread real-world clinical applications.

Indexed as

Diabetic RetinopathyFundus OculiDeep LearningHumansImage Processing, Computer-AssistedIntelligent Systems

Identifiers

PMID41922360
PMCPMC13201777

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.