Evidence map›Paper›PMID 40438493›Full record

ArticleThe EPMA journal2025

Vision transformer-based stratification of pre/diabetic and pre/hypertensive patients from retinal photographs for 3PM applications.

Krithi Pushpanathan, Yang Bai, Xiaofeng Lei, Jocelyn Hui Lin Goh, Can Can Xue, Samantha Min Er Yew, Miaoli Chee, Ten Cheer Quek, Qingsheng Peng, Zhi Da Soh and 12 more

Abstract read
In one paragraph

Article in The EPMA journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

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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

22 authors.

Krithi Pushpanathan *Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Yang Bai *Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.
Xiaofeng LeiInstitute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.
Jocelyn Hui Lin GohSingapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
Can Can XueSingapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
Samantha Min Er YewDepartment of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Miaoli CheeSingapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
Ten Cheer QuekSingapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
Qingsheng PengSingapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
Zhi Da SohSingapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
Marco Chak Yan YuSingapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
Jun ZhouInstitute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.
Yaxing WangOphthalmology and Visual Science Key Lab, Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, BeijingBeijing, China.
Jost B JonasSingapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
Xiaofei WangKey Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
Xueling SimSaw Swee Hock School of Public Health, National University of Singaporeand, National University Health System , Singapore, Singapore.
E Shyong TaiSaw Swee Hock School of Public Health, National University of Singaporeand, National University Health System , Singapore, Singapore.
Charumathi SabanayagamSingapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
Rick Siow Mong GohInstitute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.
Yong Liu *Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.
Ching-Yu Cheng *Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Yih-Chung Tham *Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Diabetes and hypertension pose significant health risks, especially when poorly managed. Retinal evaluation though fundus photography can provide non-invasive assessment of these diseases, yet prior studies focused on disease presence, overlooking control statuses. This study evaluated vision transformer (ViT)-based models for assessing the presence and control statuses of diabetes and hypertension from retinal images. Methods: ViT-based models with ResNet-50 for patch projection were trained on images from the UK Biobank ( Results: The models demonstrated strong performance in detecting disease presence, with AUROC values of 0.820 for diabetes and 0.781 for hypertension in internal testing. External validation showed AUROCs ranging from 0.635 to 0.755 for diabetes, and 0.727 to 0.832 for hypertension. For identifying poorly controlled cases, the performance remained high with AUROCs of 0.871 (internal) and 0.655-0.851 (external) for diabetes, and 0.853 (internal) and 0.792-0.915 (external) for hypertension. Detection of well-controlled cases also yielded promising results for diabetes (0.802 [internal]; 0.675-0.838 [external]), and hypertension (0.740 [internal] and 0.675-0.807 [external]). In distinguishing between poorly and well-controlled disease, AUROCs were more modest with 0.630 (internal) and 0.512-0.547 (external) for diabetes, and 0.651 (internal) and 0.639-0.683 (external) for hypertension. For pre-disease detection, the models achieved AUROCs of 0.746 (internal) and 0.523-0.590 (external) for pre-diabetes, and 0.669 (internal) and 0.645-0.679 (external) for pre-hypertension. Conclusion: ViT-based models show promise in classifying the presence and control statuses of diabetes and hypertension from retinal images. These findings support the potential of retinal imaging as a tool in primary care for opportunistic detection of diabetes and hypertension, risk stratification, and individualised treatment planning. Further validation in diverse clinical settings is warranted to confirm practical utility. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-025-00412-9.

Indexed as

Deep learningDiabetesHealth risk assessmentHypertensionImproved individual outcomesInnovative screening programsOpportunistic screeningPredictive Preventive Personalized Medicine (PPPM / 3PM)Preventable diseasesProtection against health-to-disease transitionRetinal imageRisk stratification

Identifiers

PMID40438493
PMCPMC12106178

What Socratic holds

Textmetadata
LicenceCC BY
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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 Socratic graph.