Evidence map›Paper›PMID 41623677›Full record

ReviewFrontiers in ophthalmology2025

Diagnostic challenges in high myopia: identification of sight-threatening complications and the role of artificial intelligence.

Shiqi Zhang, Jiaqi Chen, Hongli Yang, Huiping Yuan

Abstract readReview
In one paragraph

Review in Frontiers in ophthalmology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Shiqi ZhangDepartment of Ophthalmology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Jiaqi ChenDepartment of Ophthalmology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Hongli YangDevers Eye Institute, Legacy Health Research, Portland, OR, United States.
Huiping YuanDepartment of Ophthalmology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High myopia (HM), defined as a spherical equivalent refractive error ≤ -5.00 or ≤ -6.00 diopters or axial length (AL) ≥ 26.0 mm, is a significant public health concern with a rapidly increasing prevalence, particularly in East Asia. Beyond impaired uncorrected vision, HM is associated with sight-threatening structural changes, including myopic maculopathy, choroidal neovascularization, retinal detachment, and glaucoma. The overlapping and atypical presentations of these complications pose considerable diagnostic challenges, often delaying intervention and complicating clinical management. This review synthesizes current knowledge on HM, emphasizing the spectrum of ocular complications and the multifaceted diagnostic dilemmas encountered. We have summarized the application of conventional and emerging diagnostic techniques-such as optical coherence tomography (OCT), ultra-widefield imaging, and fluorescein angiography in the diagnosis of high myopia and highlight the growing role of artificial intelligence (AI) and machine learning in enhancing diagnostic accuracy, particularly through the analysis of retinal images and OCT data. AI-based systems demonstrate high sensitivity and specificity in detecting HM-related pathologies, offering potential for large-scale screening and early intervention. Future directions include the development of integrated multimodal imaging platforms, genetic and metabolic biomarkers, and AI-driven predictive models to support personalized management strategies. This comprehensive overview underscores the need for advanced, accessible diagnostic tools to alleviate the burden associated with high myopia.

Indexed as

artificial intelligence (AI)complication diagnosishigh myopiaoptical coherence tomography (OCT)pathologic myopia

Identifiers

PMID41623677
PMCPMC12851988

What Socratic 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 Socratic graph.