Evidence map›Paper›PMID 41882586›Full record

Observational studyBMC ophthalmology2026

Study on fundus characteristics of preschool children based on artificial intelligence quantitative technology.

Yuchen Li, Chengyue Zhang, Xingye Wang, Siyuan Li, Di Cao, Chang Liu, Xue Zhang, Wen Liu, Ran Du, Xiaona Sun and 5 more

Abstract readObservational Study
In one paragraph

Observational study in BMC ophthalmology, 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.

Yuchen LiDepartment of Ophthalmology, Key Laboratory of Major Diseases in Children, Ministry of Education, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.
Chengyue ZhangDepartment of Ophthalmology, Key Laboratory of Major Diseases in Children, Ministry of Education, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.
Xingye WangEVisionTechnology (Beijing) Co.Ltd, Shangdixinxi Road No. 26, Haidian District, Beijing, China.
Siyuan LiDepartment of Ophthalmology, Key Laboratory of Major Diseases in Children, Ministry of Education, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.
Di CaoDepartment of Ophthalmology, Key Laboratory of Major Diseases in Children, Ministry of Education, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.
Chang LiuDepartment of Ophthalmology, Key Laboratory of Major Diseases in Children, Ministry of Education, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.
Xue ZhangEVisionTechnology (Beijing) Co.Ltd, Shangdixinxi Road No. 26, Haidian District, Beijing, China.
Wen LiuDepartment of Ophthalmology, Key Laboratory of Major Diseases in Children, Ministry of Education, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.
Ran DuDepartment of Ophthalmology, Key Laboratory of Major Diseases in Children, Ministry of Education, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.
Xiaona SunDepartment of Ophthalmology, Key Laboratory of Major Diseases in Children, Ministry of Education, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.
Yanan GuoDepartment of Ophthalmology, Key Laboratory of Major Diseases in Children, Ministry of Education, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.
Liping ZhangDepartment of Ophthalmology, Key Laboratory of Major Diseases in Children, Ministry of Education, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.
Ying WangDepartment of Ophthalmology, Key Laboratory of Major Diseases in Children, Ministry of Education, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.
Yunwei FanDepartment of Ophthalmology, Key Laboratory of Major Diseases in Children, Ministry of Education, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China. 13520950565@163.com.
Li LiDepartment of Ophthalmology, Key Laboratory of Major Diseases in Children, Ministry of Education, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China. lili@bch.com.cn.

Funding

National Natural Science Foundation of China 81970844
6 · The paper itself

Abstract

backgroundThe study aimed to establish the first artificial intelligence (AI)-based quantitative reference standard for fundus characteristics in 3–5-year-old preschool children, analyzing microstructural features and their correlations with myopia biomarkers to inform age-stratified standards and early myopia warning.

methodsThis cross-sectional observational study involved healthy preschoolers (3–5 years) without ocular/systemic disorders affecting refraction. 45° fovea-centered fundus images were captured via handheld camera, and a TransUnet model automated segmentation/quantification of optic disc/cup, retinal vasculature, and fundus tessellated density (FTD). Refractive parameters including spherical equivalent (SE), axial length (AL), and corneal curvature were measured, with statistical analyses exploring age-related variations and correlations.

resultsAge-stratified reference intervals were defined. Optic disc/cup parameters (median area: 2.60 mm², Cup-to-Disc Area Ratio: 0.23) showed no age-group differences (P > 0.05). Retinal vascular parameters varied with age, while macular FTD exhibited zonal age differences (P < 0.05) and correlated positively with AL (r = 0.141–0.207, P < 0.05). SE associated positively with vessel density/tortuosity (r = 0.128–0.280, P < 0.05), but FTD did not correlate with SE (P > 0.05).

conclusionsThis study integrated AI with fundus imaging to establish quantitative fundus parameter datasets, defining age-stratified references and refractive correlations. Findings provide a scientific basis for pediatric myopia early warning and preventive interventions by identifying anatomical correlations between fundus parameters and refractive development.

Indexed as

Artificial IntelligenceFundus OculiMyopiaOptic DiskRetinal VesselsAxial Length, EyeChild, PreschoolCross-Sectional StudiesFemaleHumansMaleReference ValuesRefraction, OcularArtificial intelligence-based quantitative technologyFundus developmentFundus tessellated densityMyopia risk predictionPreschool children

Identifiers

PMID41882586
PMCPMC13188452

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.