ArticleNature communications2023
LensAge index as a deep learning-based biological age for self-monitoring the risks of age-related diseases and mortality.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Transformative applications of oculomics-based AI approaches in the management of systemic diseases: A systematic review.Journal of advanced research · 2025Pooled it
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- Factors Associated with Machine Learning-Based Predictions of Retinal Aging Using Teleretinal Screening Images from Patients with Diabetes.Ophthalmology science · 2026Article
- Impacts of leukocyte telomere length on incidence and severity of age-related cataract: a cross-cohort analysis.Eye and vision (London, England) · 2025Article
- Accelerated Biological Aging and Genetic Pleiotropy in Age-Related Eye Diseases: A Population-Based Cohort and Integrative Genetic Analysis.Investigative ophthalmology & visual science · 2025Article
- Predictive Modeling of Cycloplegic Refraction Using Non-Cycloplegia Ocular Parameters With Emphasis on Lens-Related Features.Translational vision science & technology · 2025Article
- Association between mental work and biological aging: the mediating role of visceral adiposity index.Innovation in aging · 2025Article
- Article
- Integrating aging biomarkers and immune function to predict kidney health: insights from the future of families and child wellbeing study.GeroScience · 2025Article
- Deep learning and generative artificial intelligence in aging research and healthy longevity medicine.Aging · 2025Review
- Biological age in critical care: current evidence, future prospects, and clinical implications.Frontiers in medicine · 2025Review
- Perceived age estimation from facial image and demographic data in young and middle-aged South Korean adults.Scientific reports · 2024Article
- Effectiveness and biocompatibility of a novel Schlemm's canal microstent for glaucoma management.Scientific reports · 2024Article
- Application of a deep-learning marker for morbidity and mortality prediction derived from retinal photographs: a cohort development and validation study.The lancet. Healthy longevity · 2024Article
- Development and research status of intelligent ophthalmology in China.International journal of ophthalmology · 2024Review
- Integration of smartphone technology and artificial intelligence for advanced ophthalmic care: A systematic review.Advances in ophthalmology practice and researchReview
Corrections and comments
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Authors and funding
27 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Age is closely related to human health and disease risks. However, chronologically defined age often disagrees with biological age, primarily due to genetic and environmental variables. Identifying effective indicators for biological age in clinical practice and self-monitoring is important but currently lacking. The human lens accumulates age-related changes that are amenable to rapid and objective assessment. Here, using lens photographs from 20 to 96-year-olds, we develop LensAge to reflect lens aging via deep learning. LensAge is closely correlated with chronological age of relatively healthy individuals (R
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