Evidence map›Paper›PMID 38074310›Full record

ArticleSaudi journal of ophthalmology : official journal of the Saudi Ophthalmological Society

Vision transformers: The next frontier for deep learning-based ophthalmic image analysis.

Jo-Hsuan Wu, Neslihan D Koseoglu, Craig Jones, T Y Alvin Liu

Abstract read
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In one paragraph

Article in Saudi journal of ophthalmology : official journal of the Saudi Ophthalmological Society. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. AI-driven multimodal retinal imaging for early detection and risk stratification of vascular and neurodegenerative diseases.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026
    Review
  2. Article
  3. Article
  4. Article
  5. Review
  6. Review
  7. Article
  8. Ophthalmology's new horizon: Moving from reactive care to proactive artificial intelligence solutions.Saudi journal of ophthalmology : official journal of the Saudi Ophthalmological Society
    Article
  9. Review
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.

Jo-Hsuan WuDepartment of Ophthalmology, Shiley Eye Institute and Viterbi Family, University of California, San Diego, La Jolla, CA, USA.
Neslihan D KoseogluDepartment of Ophthalmology, Wilmer Eye Institute, Johns Hopkins University, Baltimore, MD, USA.
Craig JonesDepartment of Ophthalmology, Wilmer Eye Institute, Johns Hopkins University, Baltimore, MD, USA.
T Y Alvin LiuDepartment of Ophthalmology, Wilmer Eye Institute, Johns Hopkins University, Baltimore, MD, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Deep learning is the state-of-the-art machine learning technique for ophthalmic image analysis, and convolutional neural networks (CNNs) are the most commonly utilized approach. Recently, vision transformers (ViTs) have emerged as a promising approach, one that is even more powerful than CNNs. In this focused review, we summarized studies that applied ViT-based models to analyze color fundus photographs and optical coherence tomography images. Overall, ViT-based models showed robust performances in the grading of diabetic retinopathy and glaucoma detection. While some studies demonstrated that ViTs were superior to CNNs in certain contexts of use, it is unclear how widespread ViTs will be adopted for ophthalmic image analysis, since ViTs typically require even more training data as compared to CNNs. The studies included were identified from the PubMed and Google Scholar databases using keywords relevant to this review. Only original investigations through March 2023 were included.

Indexed as

Color fundus photographsdeep learningophthalmic image analysisoptical coherence tomographyvision transformers

Identifiers

PMID38074310
PMCPMC10701151

What OpenQuestion holds

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LicenceCC BY-NC-SA
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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.