ArticleNPJ digital medicine2025
A cross population study of retinal aging biomarkers with longitudinal pre-training and label distribution learning.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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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.
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Who cites it
16 citing papers in PubMed.
- Deep learning-derived retinal age gap and its associations with lifestyle, systemic, and ocular health in a health screening cohort.GeroScience · 2026Article
- Deep Learning-Derived Retinal Age Detects Cognitive Impairment.Ophthalmology science · 2026Article
- Review
- Proteomic clocks combined with deep learning phenotypes track eye aging and diseases.NPJ digital medicine · 2026Article
- Macula- Versus Disc-Centered Fundus Photography: Performance in Age-Prediction and Disease Associations.Investigative ophthalmology & visual science · 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
- Vascular Aging.Circulation · 2026Review
- The retina-body axis: proteomic mechanisms linking oculomics and clinical traits in a female aging cohort.npj aging · 2026Article
- High-accuracy retinal age prediction via fundus-based multitask learning reveals the effect of systemic disease.Communications medicine · 2026Article
- Factors Associated with Machine Learning-Based Predictions of Retinal Aging Using Teleretinal Screening Images from Patients with Diabetes.Ophthalmology science · 2026Article
- Modifiable risk factors associated with increased retinal age gap in an Australian population.GeroScience · 2026Article
- Age-Related Associations of Foveal Structural Parameters in Healthy Adults: A Comparative Analysis of Biological and Chronological Age.Vision (Basel, Switzerland) · 2026Article
- 2025 Advances in Brain Health: Brain Health as Capital for Vascular and Cognitive Resilience.Stroke · 2026Article
- Artificial intelligence-integrated multimodal retinal imaging for early detection and risk stratification of systemic vascular and neurodegenerative diseases.Frontiers in neurology · 2026Article
- Oculomics meets exposomics: a roadmap for applying multi-modal ocular biomarkers in precision environmental health research.Exposome · 2025Review
- Estimation of biological aging based on T-cell differentiation trajectories: emerging and future avenues.Frontiers in aging · 2025Article
Corrections and comments
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Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Retinal age has emerged as a promising biomarker of aging, offering a non-invasive and accessible assessment tool. We developed a deep learning model to estimate retinal age with enhanced accuracy, leveraging retinal images from diverse populations. Our approach integrates self-supervised learning to capture chronological information from both snapshot and sequential images, alongside a progressive label distribution learning module to model biological aging variability. Trained and validated on healthy cohorts (34,433 participants from the UK Biobank and three Chinese cohorts), the model achieved a mean absolute error of 2.79 years, surpassing previous methods. When applied to broader populations, analysis of the retinal age gap-the difference between retina-predicted and chronological age-revealed associations with increased risks of all-cause mortality and multiple age-related diseases. These findings highlight the potential of retinal age as a reliable biomarker for predicting survival and aging outcomes, supporting targeted risk management and precision health interventions.
Identifiers
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Registered trials
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.