Evidence map›Paper›PMID 41133975›Full record

Trial reportOphthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists)2025

Deep learning-empowered low-cost portable automated refraction system: A solution to the inadequate effective correction rate of refractive errors in resource-limited areas.

Huang Yan, Zhen Yi, Sun Qilin, Chang Hong, Huang Yan, Tang Wei

Abstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists), 2025. 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. Deep learning-empowered low-cost portable automated refraction system: A solution to the inadequate effective correction rate of refractive errors in resource-limited areas.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2025
    Trial
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

6 authors.

Huang YanNational Engineering Research Centre for Ophthalmology, Beijing Institute of Ophthalmology, Beijing Tongren Eye Centre, Engineering Research Centre of the Ministry of Education for Ophthalmic Diagnosis and Treatment Equipment and Materials, Beijing Key Laboratory of Ophthalmology and Visual Science, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Zhen YiNational Engineering Research Centre for Ophthalmology, Beijing Institute of Ophthalmology, Beijing Tongren Eye Centre, Engineering Research Centre of the Ministry of Education for Ophthalmic Diagnosis and Treatment Equipment and Materials, Beijing Key Laboratory of Ophthalmology and Visual Science, Beijing Tongren Hospital, Capital Medical University, Beijing, China.ORCID https://orcid.org/0000-0002-9031-1245
Sun QilinState Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences (CASIA), Beijing, China.
Chang HongBeijing, He Eye Specialist Hospital, Beijing, China.
Huang YanState Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences (CASIA), Beijing, China.
Tang WeiState Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences (CASIA), Beijing, China.

Funding

Beijing Municipal Science and Technology Project Z201100005520042
6 · The paper itself

Abstract

aimsTo develop and validate a low-cost, portable automated refraction system (Tongren Digital Sight, TRDS) using deep learning and infrared eccentric photorefraction to improve refractive error correction in resource-constrained settings.

methodsThis randomised controlled crossover trial enrolled 282 participants (18-60 years of age) at Beijing Tongren Hospital. TRDS utilised low-cost components (complementary metal-oxide semiconductor (CMOS) detector, infrared light emitting diodes) and an end-to-end deep learning model trained on 362,000 images to analyse fundus reflection signals. Primary outcomes included agreement between TRDS and subjective refraction (gold standard) via Pearson correlation, Bland-Altman plots and intraclass correlation coefficients (ICC) for spherical equivalent (M), horizontal (J

resultsTRDS showed strong correlations with subjective refraction: M (r = 0.96, ICC = 0.96), J

conclusionsThe deep learning-empowered TRDS system demonstrated high accuracy and portability. It offers a scalable solution to improve refractive error correction rates, aligning with WHO goals to enhance global eye health.

Indexed as

Deep LearningRefraction, OcularRefractive ErrorsVisual AcuityAdolescentAdultCross-Over StudiesEquipment DesignFemaleHumansMaleMiddle AgedYoung Adultdeep learningeffective correction ratelow‐costrefractive errorsresource‐limited areas

Identifiers

PMID41133975
PMCPMC12682091

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.