Evidence map›Paper›PMID 38969362›Full record

ArticleBMJ open ophthalmology2024

Quantitative assessment of colour fundus photography in hyperopia children based on artificial intelligence.

Ruiyu Luo, Zhirong Wang, Zhidong Li, Rui Xie, Yuan Zhang, Guitong Ye, Jianqi Chen, Yue Xiao, Jinan Zhan, Yingting Zhu and 1 more

Abstract read
In one paragraph

Article in BMJ open ophthalmology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Observational
  2. 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

11 authors.

Ruiyu LuoOphthalmic Center State Key Laboratory of Ophthalmology, Sun Yat-Sen University Zhongshan, Guangzhou, Guangdong, China.ORCID 0000-0002-0602-288X
Zhirong WangOphthalmic Center State Key Laboratory of Ophthalmology, Sun Yat-Sen University Zhongshan, Guangzhou, Guangdong, China.
Zhidong LiOphthalmic Center State Key Laboratory of Ophthalmology, Sun Yat-Sen University Zhongshan, Guangzhou, Guangdong, China.
Rui XieOphthalmic Center State Key Laboratory of Ophthalmology, Sun Yat-Sen University Zhongshan, Guangzhou, Guangdong, China.
Yuan ZhangOphthalmic Center State Key Laboratory of Ophthalmology, Sun Yat-Sen University Zhongshan, Guangzhou, Guangdong, China.
Guitong YeOphthalmic Center State Key Laboratory of Ophthalmology, Sun Yat-Sen University Zhongshan, Guangzhou, Guangdong, China.
Jianqi ChenOphthalmic Center State Key Laboratory of Ophthalmology, Sun Yat-Sen University Zhongshan, Guangzhou, Guangdong, China.
Yue XiaoOphthalmic Center State Key Laboratory of Ophthalmology, Sun Yat-Sen University Zhongshan, Guangzhou, Guangdong, China.
Jinan ZhanOphthalmic Center State Key Laboratory of Ophthalmology, Sun Yat-Sen University Zhongshan, Guangzhou, Guangdong, China.
Yingting ZhuOphthalmic Center State Key Laboratory of Ophthalmology, Sun Yat-Sen University Zhongshan, Guangzhou, Guangdong, China.
Yehong ZhuoOphthalmic Center State Key Laboratory of Ophthalmology, Sun Yat-Sen University Zhongshan, Guangzhou, Guangdong, China zhuoyh@mail.sysu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThis study aimed to quantitatively evaluate optic nerve head and retinal vascular parameters in children with hyperopia in relation to age and spherical equivalent refraction (SER) using artificial intelligence (AI)-based analysis of colour fundus photographs (CFP). METHODS AND ANALYSIS: This cross-sectional study included 324 children with hyperopia aged 3-12 years. Participants were divided into low hyperopia (SER+0.5 D to+2.0 D) and moderate-to-high hyperopia (SER≥+2.0 D) groups. Fundus parameters, such as optic disc area and mean vessel diameter, were automatically and quantitatively detected using AI. Significant variables (p<0.05) in the univariate analysis were included in a stepwise multiple linear regression.

resultsOverall, 324 children were included, 172 with low and 152 with moderate-to-high hyperopia. The median optic disc area and vessel diameter were 1.42 mm

conclusionAI-based CFP analysis showed that children with high hyperopia had larger mean vessel diameter but smaller vertical cup-to-disc ratio than those with low hyperopia. This suggests that AI can provide quantitative data on fundus parameters in children with hyperopia.

Indexed as

Artificial IntelligenceHyperopiaOptic DiskPhotographyRetinal VesselsChildChild, PreschoolCross-Sectional StudiesFemaleFundus OculiHumansMaleRefraction, OcularVisual AcuityChild health (paediatrics)ImagingOptic NerveOptics and RefractionRetina

Identifiers

PMID38969362
PMCPMC11227822

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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.