Evidence map›Paper›PMID 40269122›Full record

ArticleCommunications medicine2025

Artificial intelligence assisted clinical fluorescence imaging achieves in vivo cellular resolution comparable to adaptive optics ophthalmoscopy.

Joanne Li, Jianfei Liu, Vineeta Das, Hong Le, Nancy Aguilera, Andrew J Bower, John P Giannini, Rongwen Lu, Sarah Abouassali, Emily Y Chew and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Communications medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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

16 authors.

Joanne LiNational Eye Institute, National Institutes of Health, Bethesda, MD, 20892, USA.ORCID http://orcid.org/0000-0003-2845-2490
Jianfei LiuNational Eye Institute, National Institutes of Health, Bethesda, MD, 20892, USA.ORCID http://orcid.org/0000-0001-9885-1695
Vineeta DasNational Eye Institute, National Institutes of Health, Bethesda, MD, 20892, USA.ORCID http://orcid.org/0000-0001-9114-1455
Hong LeNational Eye Institute, National Institutes of Health, Bethesda, MD, 20892, USA.ORCID http://orcid.org/0000-0002-9477-9419
Nancy AguileraNational Eye Institute, National Institutes of Health, Bethesda, MD, 20892, USA.ORCID http://orcid.org/0000-0003-0863-596X
Andrew J BowerNational Eye Institute, National Institutes of Health, Bethesda, MD, 20892, USA.ORCID http://orcid.org/0000-0003-1645-5950
John P GianniniNational Eye Institute, National Institutes of Health, Bethesda, MD, 20892, USA.ORCID http://orcid.org/0000-0001-7626-6540
Rongwen LuNational Eye Institute, National Institutes of Health, Bethesda, MD, 20892, USA.ORCID http://orcid.org/0000-0002-0224-7969
Sarah AbouassaliNational Eye Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
Emily Y ChewNational Eye Institute, National Institutes of Health, Bethesda, MD, 20892, USA.ORCID http://orcid.org/0000-0003-0999-9802
Brian P BrooksNational Eye Institute, National Institutes of Health, Bethesda, MD, 20892, USA.ORCID http://orcid.org/0000-0002-1916-7551
Wadih M ZeinNational Eye Institute, National Institutes of Health, Bethesda, MD, 20892, USA.ORCID http://orcid.org/0000-0002-3771-6120
Laryssa A HurynNational Eye Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
Andrei VolkovNational Eye Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
Tao LiuNational Eye Institute, National Institutes of Health, Bethesda, MD, 20892, USA.ORCID http://orcid.org/0000-0001-9864-3896
Johnny TamNational Eye Institute, National Institutes of Health, Bethesda, MD, 20892, USA. johnny@nih.gov.ORCID http://orcid.org/0000-0003-2300-0567

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAdvancements in biomedical optical imaging have enabled researchers to achieve cellular-level imaging in the living human body. However, research-grade technology is not always widely available in routine clinical practice. In this paper, we incorporated artificial intelligence (AI) with standard clinical imaging to successfully obtain images of the retinal pigment epithelial (RPE) cells in living human eyes.

methodsFollowing intravenous injection of indocyanine green (ICG) dye, subjects were imaged by both conventional instruments and adaptive optics (AO) ophthalmoscopy. To improve the visibility of RPE cells in conventional ICG images, we demonstrate both a hardware approach using a custom lens add-on and an AI-based approach using a stratified cycleGAN network.

resultsWe observe similar fluorescent mosaic patterns arising from labeled RPE cells on both conventional and AO images, suggesting that cellular-level imaging of RPE may be obtainable using conventional imaging, albeit at lower resolution. Results show that higher resolution ICG RPE images of both healthy and diseased eyes can be obtained from conventional images using AI with a potential 220-fold improvement in time.

conclusionsThe application of using AI as an add-on module for existing instrumentation is an important step towards routine screening and detection of disease at earlier stages.

Identifiers

PMID40269122
PMCPMC12019174

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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.