ArticleAge and ageing2022
Retinal photograph-based deep learning predicts biological age, and stratifies morbidity and mortality risk.
Article in Age and ageing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 1 of them a synthesis that pooled it.
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Who cites it
54 citing papers in PubMed, 1 synthesis or guideline pooled it, 85 citations in OpenAlex.
- A Systematic Review and Meta-Analysis of Applying Deep Learning in the Prediction of the Risk of Cardiovascular Diseases From Retinal Images.Translational vision science & technology · 2023Pooled it
- Opportunistic Cardiovascular Risk Assessment in the Eye Clinic: Validation of a Retinal Deep Learning-Based 10-Year Atherosclerotic Cardiovascular Disease Estimator.Ophthalmology science · 2026Article
- Interpreting ocular age estimates: source, output, evidence, and clinical meaning.Biogerontology · 2026Review
- Retinal age gap in unilateral retinal diseases with unaffected fellow eye: phakia and vascular pathology impact retinal age prediction.Japanese journal of ophthalmology · 2026Article
- AI-driven multimodal retinal imaging for early detection and risk stratification of vascular and neurodegenerative diseases.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Deep learning aging marker from retinal images unveils sex-specific clinical and genetic signatures.Nature communications · 2026Article
- Review
- Functional, molecular, and digital measurements of biological age.The Journal of clinical investigation · 2026Review
- Macula- Versus Disc-Centered Fundus Photography: Performance in Age-Prediction and Disease Associations.Investigative ophthalmology & visual science · 2026Article
- Article
- Retinal biological age correlates with bone mineral density and fracture risk score and predicts incident osteoporosis.PLOS digital health · 2026Article
- Deep learning detection of retinitis pigmentosa inheritance forms through synthetic data expansion of a rare disease dataset.Scientific reports · 2026Article
- High-accuracy retinal age prediction via fundus-based multitask learning reveals the effect of systemic disease.Communications medicine · 2026Article
- Deep Ensemble Learning to Detect Retinal Vascular Leakage on Ultrawide-Field Fundus Photographs of Patients With Uveitis.Translational vision science & technology · 2026Article
- Factors Associated with Machine Learning-Based Predictions of Retinal Aging Using Teleretinal Screening Images from Patients with Diabetes.Ophthalmology science · 2026Article
- Age-Related Associations of Foveal Structural Parameters in Healthy Adults: A Comparative Analysis of Biological and Chronological Age.Vision (Basel, Switzerland) · 2026Article
- Retinal BioAge is associated with indicators of cardiovascular-kidney-metabolic syndrome in UK and US populations.Scientific reports · 2026Article
- Spine age derived from DXA vertebral fracture assessment images predicts incident fractures and mortality: the Manitoba Bone Mineral Density Registry.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2026Article
- Prospective real-world implementation of deep learning systems in healthcare: a systematic review guided by implementation science.NPJ digital medicine · 2026Article
- Deep learning-based retinal risk stratification for 10-year atherosclerotic cardiovascular mortality in patients with hypertension and diabetes: a retrospective cohort study.Clinical hypertension · 2026Article
Corrections and comments
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Authors and funding
17 authors at 7 institutions in 2 countries.
Funding
Abstract
backgroundageing is an important risk factor for a variety of human pathologies. Biological age (BA) may better capture ageing-related physiological changes compared with chronological age (CA).
objectivewe developed a deep learning (DL) algorithm to predict BA based on retinal photographs and evaluated the performance of our new ageing marker in the risk stratification of mortality and major morbidity in general populations.
methodswe first trained a DL algorithm using 129,236 retinal photographs from 40,480 participants in the Korean Health Screening study to predict the probability of age being ≥65 years ('RetiAGE') and then evaluated the ability of RetiAGE to stratify the risk of mortality and major morbidity among 56,301 participants in the UK Biobank. Cox proportional hazards model was used to estimate the hazard ratios (HRs).
resultsin the UK Biobank, over a 10-year follow up, 2,236 (4.0%) died; of them, 636 (28.4%) were due to cardiovascular diseases (CVDs) and 1,276 (57.1%) due to cancers. Compared with the participants in the RetiAGE first quartile, those in the RetiAGE fourth quartile had a 67% higher risk of 10-year all-cause mortality (HR = 1.67 [1.42-1.95]), a 142% higher risk of CVD mortality (HR = 2.42 [1.69-3.48]) and a 60% higher risk of cancer mortality (HR = 1.60 [1.31-1.96]), independent of CA and established ageing phenotypic biomarkers. Likewise, compared with the first quartile group, the risk of CVD and cancer events in the fourth quartile group increased by 39% (HR = 1.39 [1.14-1.69]) and 18% (HR = 1.18 [1.10-1.26]), respectively. The best discrimination ability for RetiAGE alone was found for CVD mortality (c-index = 0.70, sensitivity = 0.76, specificity = 0.55). Furthermore, adding RetiAGE increased the discrimination ability of the model beyond CA and phenotypic biomarkers (increment in c-index between 1 and 2%).
conclusionsthe DL-derived RetiAGE provides a novel, alternative approach to measure ageing.
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Registered trials
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