Evidence map›Paper›PMID 38600290›Full record

ArticleCommunications medicine2024

Revealing speckle obscured living human retinal cells with artificial intelligence assisted adaptive optics optical coherence tomography.

Vineeta Das, Furu Zhang, Andrew J Bower, Joanne Li, Tao Liu, Nancy Aguilera, Bruno Alvisio, Zhuolin Liu, Daniel X Hammer, Johnny Tam

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Communications medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
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  5. Article
  6. Review
  7. Article
  8. Frontiers in ophthalmology · 2025
    Review
  9. Article
  10. Article
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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

10 authors.

Vineeta DasNational Eye Institute, National Institutes of Health, Bethesda, MD, 20892, USA.ORCID http://orcid.org/0000-0001-9114-1455
Furu ZhangNational Eye Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
Andrew J BowerNational Eye Institute, National Institutes of Health, Bethesda, MD, 20892, USA.ORCID http://orcid.org/0000-0003-1645-5950
Joanne LiNational Eye Institute, National Institutes of Health, Bethesda, MD, 20892, USA.ORCID http://orcid.org/0000-0003-2845-2490
Tao LiuNational Eye Institute, National Institutes of Health, Bethesda, MD, 20892, USA.ORCID http://orcid.org/0000-0001-9864-3896
Nancy AguileraNational Eye Institute, National Institutes of Health, Bethesda, MD, 20892, USA.ORCID http://orcid.org/0000-0003-0863-596X
Bruno AlvisioNational Eye Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
Zhuolin LiuCenter for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, 20993, USA.ORCID http://orcid.org/0000-0001-8019-2054
Daniel X HammerCenter for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, 20993, USA.ORCID http://orcid.org/0000-0003-0532-1914
Johnny TamNational Eye Institute, National Institutes of Health, Bethesda, MD, 20892, USA. johnny@nih.gov.ORCID http://orcid.org/0000-0003-2300-0567

Funding

Adaptive Optics Retinal ImagingZIAEY000544 · NEI · NATIONAL EYE INSTITUTE · PI TAM, JOHNNY · 2015 to 2025
$24.0M
6 · The paper itself

Abstract

backgroundIn vivo imaging of the human retina using adaptive optics optical coherence tomography (AO-OCT) has transformed medical imaging by enabling visualization of 3D retinal structures at cellular-scale resolution, including the retinal pigment epithelial (RPE) cells, which are essential for maintaining visual function. However, because noise inherent to the imaging process (e.g., speckle) makes it difficult to visualize RPE cells from a single volume acquisition, a large number of 3D volumes are typically averaged to improve contrast, substantially increasing the acquisition duration and reducing the overall imaging throughput.

methodsHere, we introduce parallel discriminator generative adversarial network (P-GAN), an artificial intelligence (AI) method designed to recover speckle-obscured cellular features from a single AO-OCT volume, circumventing the need for acquiring a large number of volumes for averaging. The combination of two parallel discriminators in P-GAN provides additional feedback to the generator to more faithfully recover both local and global cellular structures. Imaging data from 8 eyes of 7 participants were used in this study.

resultsWe show that P-GAN not only improves RPE cell contrast by 3.5-fold, but also improves the end-to-end time required to visualize RPE cells by 99-fold, thereby enabling large-scale imaging of cells in the living human eye. RPE cell spacing measured across a large set of AI recovered images from 3 participants were in agreement with expected normative ranges.

conclusionsThe results demonstrate the potential of AI assisted imaging in overcoming a key limitation of RPE imaging and making it more accessible in a routine clinical setting.

Identifiers

PMID38600290
PMCPMC11006674

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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.