Evidence map›Paper›PMID 38514852›Full record

ReviewEye (London, England)2024

Diagnosing glaucoma in primary eye care and the role of Artificial Intelligence applications for reducing the prevalence of undetected glaucoma in Australia.

Catherine Jan, Mingguang He, Algis Vingrys, Zhuoting Zhu, Randall S Stafford

Open access · hybridAbstract readReview
In one paragraph

Review in Eye (London, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
10.8field-weighted citation impact, top 1% of its field
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

12 citing papers in PubMed, 20 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Analysing diagnostic practices and referral pathways for glaucoma in Australian primary eye care.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2025
    Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. 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

5 authors at 3 institutions in 3 countries.

Catherine JanCentre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, Australia. cjan541@gmail.com.ORCID http://orcid.org/0000-0001-7383-8208
Mingguang HeCentre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, Australia.
Algis VingrysCentre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, Australia.
Zhuoting ZhuCentre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, Australia.ORCID http://orcid.org/0000-0002-9897-1192
Randall S StaffordStanford Prevention Research Center, Stanford University School of Medicine, Stanford, CA, USA.ORCID http://orcid.org/0000-0003-1805-1271
The University of Melbourne · AUHong Kong Polytechnic University · HKStanford University · US

Funding

Department of Health | National Health and Medical Research Council (NHMRC) 42513
6 · The paper itself

Abstract

Glaucoma is the commonest cause of irreversible blindness worldwide, with over 70% of people affected remaining undiagnosed. Early detection is crucial for halting progressive visual impairment in glaucoma patients, as there is no cure available. This narrative review aims to: identify reasons for the significant under-diagnosis of glaucoma globally, particularly in Australia, elucidate the role of primary healthcare in glaucoma diagnosis using Australian healthcare as an example, and discuss how recent advances in artificial intelligence (AI) can be implemented to improve diagnostic outcomes. Glaucoma is a prevalent disease in ageing populations and can have improved visual outcomes through appropriate treatment, making it essential for general medical practice. In countries such as Australia, New Zealand, Canada, USA, and the UK, optometrists serve as the gatekeepers for primary eye care, and glaucoma detection often falls on their shoulders. However, there is significant variation in the capacity for glaucoma diagnosis among eye professionals. Automation with Artificial Intelligence (AI) analysis of optic nerve photos can help optometrists identify high-risk changes and mitigate the challenges of image interpretation rapidly and consistently. Despite its potential, there are significant barriers and challenges to address before AI can be deployed in primary healthcare settings, including external validation, high quality real-world implementation, protection of privacy and cybersecurity, and medico-legal implications. Overall, the incorporation of AI technology in primary healthcare has the potential to reduce the global prevalence of undiagnosed glaucoma cases by improving diagnostic accuracy and efficiency.

Indexed as

Artificial IntelligenceGlaucomaPrimary Health CareAustraliaHumansPrevalence

Identifiers

PMID38514852
PMCPMC11269618
OpenAlexW4393037410

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.