Evidence map›Paper›PMID 41807485›Full record

ArticleScientific data2026

SYN-OCT:A synthetic dataset of ocular optical coherence tomography images from healthy and glaucoma eyes.

Damon Wong, Ashish Jith Sreejith Kumar, Rachel S Chong, Monisha E Nongpiur, Rahat Husain, Tina Wong, Shamira Perera, Tin Aung, Bingyao Tan, Ching-Yu Cheng and 4 more

Abstract readDataset
In one paragraph

Article in Scientific data, 2026. 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
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0citing papers in PubMed
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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

14 authors.

Damon WongSingapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
Ashish Jith Sreejith KumarSingapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
Rachel S ChongSingapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
Monisha E NongpiurSingapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
Rahat HusainSingapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
Tina WongSingapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
Shamira PereraSingapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
Tin AungSingapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
Bingyao TanSingapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
Ching-Yu ChengSingapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
Eranga N VithanaSingapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
Jacqueline ChuaSingapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
Alina Popa CherecheanuCarol Davila University of Medicine and Pharmacy, Bucharest, Romania.
Leopold SchmettererSingapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore. leopold.schmetterer@seri.com.sg.ORCID http://orcid.org/0000-0002-7189-1707

Funding

National Research Foundation Singapore (National Research Foundation-Prime Minister's office, Republic of Singapore) NRF-CRP24-2020-0001Singapore Eye Research Institute (SERI) I010/2024 [2082/44/2024])
6 · The paper itself

Abstract

The SYN-OCT dataset contains a total of 200,000 synthetic cross-sectional circumpapillary optical coherence tomography (OCT) images, comprising of 100,000 images from a generative image model for glaucoma eyes and 100,000 from a generative image model for healthy normal eyes. The generative image models were developed using real OCT imaging data acquired from study participants at the Singapore Eye Research Institute, and were trained with the respective real images from glaucoma or healthy eyes. Structural characteristics of the synthetic data were validated and found to be comparable with the real data using an automated segmentation approach. These measurements are provided together with the synthetic images. We envision this dataset to be useful for the development or validation of deep learning applications for glaucoma analysis and detection, and as a dataset to study the synthetic generation of medical images and their usability. This dataset is publicly available.

Indexed as

EyeGlaucomaTomography, Optical CoherenceHumans

Identifiers

PMID41807485
PMCPMC13102907

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Registered trials

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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.