Evidence map›Paper›PMID 42186033›Full record

Observational studyBMC public health2026

AI-based fundus screening of teachers: fundus tessellated density as a myopia biomarker and occupational associations.

Zhongnan Xiao, Huijuan Liu, Hongyan Wang, Min Song, Guoquan Liu, Chunhong Yin, Liwei Liu, Min Li, Jingmiao Sun, Yi Ren and 4 more

Abstract readObservational Study
In one paragraph

Observational study in BMC public health, 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

14 authors.

Zhongnan Xiao *Visual Rehabilitation Center, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China. xzn1016@126.com.
Huijuan Liu *Labor Model Health Management Center, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China.
Hongyan WangVisual Rehabilitation Center, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China.
Min SongLabor Model Health Management Center, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China.
Guoquan LiuVisual Rehabilitation Center, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China.
Chunhong YinVisual Rehabilitation Center, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China.
Liwei LiuVisual Rehabilitation Center, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China.
Min LiLabor Model Health Management Center, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China.
Jingmiao SunLabor Model Health Management Center, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China.
Yi RenLabor Model Health Management Center, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China.
Ying GuoLabor Model Health Management Center, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China.
Yu ZhongLabor Model Health Management Center, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China.
Hong YuLabor Model Health Management Center, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China.
Ying LiuVisual Rehabilitation Center, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China. liuying5322@163.com.

Funding

Science and Technology Development Special Fund, Beijing Rehabilitation Hospital, Capital Medical University 2021-071the Beijing Municipal Health Commission 2025 "High-Level Innovation and Entrepreneurship Talent Support Plan · Spring Bud Project" G202532352Youth Fund of the National Natural Science Foundation of China‌ 82401406
6 · The paper itself

Abstract

backgroundArtificial intelligence (AI)-based fundus imaging enables efficient large-scale retinal screening; however, its application in occupational populations remains limited.

objectiveTo evaluate the utility of an AI-based fundus screening system in teachers and to investigate fundus tessellated density (FTD) as a potential imaging marker associated with myopia and occupational factors.

methodsIn this prospective observational study, 648 teachers from Beijing underwent fundus photography and systemic examinations. AI algorithms were used to quantify FTD and other retinal parameters. Associations between spherical equivalent (SE), FTD, and systemic and occupational variables were assessed using univariate and multivariable regression analyses. Receiver operating characteristic (ROC) analysis was performed to evaluate the discriminative ability of FTD for high myopia.

resultsThe mean SE was - 2.43 ± 2.94 D, and mean FTD was 0.0609 ± 0.0743. FTD showed a non-uniform distribution across the macula, with higher values in the nasal quadrant. Multivariable analysis demonstrated that FTD, near-work duration at 30 cm, and ocular structural parameters were independently associated with SE (all P < 0.05). Prolonged near work (> 1 h) at 30 cm was associated with increased FTD. ROC analysis indicated moderate discriminative performance for high myopia (AUC = 0.7788 for overall FTD). In addition, FTD was associated with systemic immune-inflammatory indices, including NLR and PLR.

conclusionsAI-derived FTD may serve as a potential imaging marker associated with high myopia and occupational factors. These findings are observational and hypothesis-generating, and further longitudinal studies are required for validation.

Indexed as

Artificial IntelligenceFundus OculiMass ScreeningMyopiaAdultBeijingBiomarkersFemaleHumansMaleMiddle AgedProspective StudiesBiomarkersArtificial intelligenceDeep learningFundus imagingFundus tessellated densityMyopiaNear-workSystemic immune-inflammatory indicesTeachers

Identifiers

PMID42186033
PMCPMC13386797

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LicenceCC BY-NC-ND
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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.