ArticleThe EPMA journal2025
Vision transformer-based stratification of pre/diabetic and pre/hypertensive patients from retinal photographs for 3PM applications.
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.
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Who cites it
4 citing papers in PubMed.
- Initiating Events of Retinopathy in Spontaneously Hypertensive Rats: Microvascular Rarefaction and Functional Deficits.Hypertension (Dallas, Tex. : 1979) · 2026Article
- Explainable retinal deep learning for cardiovascular risk stratification: a multiple modality analysis framework with vascular-centric interpretability and robustness.BMC medical informatics and decision making · 2026Article
- A Multimodal Multitask Artificial Intelligence Model for Orthokeratology Contact Lens Fitting: An Integrated Framework to Enhance Lens Centration and Myopia Control Effect.Ophthalmology science · 2026Article
- Validated semi-supervised early and accurate screening for anterior segment diseases: a 3PM-guided conceptual and technological innovation.The EPMA journal · 2026Article
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22 authors.
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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.
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