ArticleAging2018
PhotoAgeClock: deep learning algorithms for development of non-invasive visual biomarkers of aging.
Article in Aging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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.
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.
Who cites it
45 citing papers in PubMed.
- In vitro, ex vivo, instrumental and clinical evaluation of a topical cream on the signs of periorbital ageing.International journal of cosmetic science · 2025Trial
- The Evolving Landscape of Clinical Aging Clocks: From Epigenetic to Multi-Omics Integration.Aging cell · 2026Review
- Face aging rate quantifies change in biological age to predict cancer outcomes.Nature communications · 2026Article
- Unraveling the complexity of skin's biological aging utilizing epigenetic clocks.Clinical epigenetics · 2026Article
- Article
- Artificial Intelligence versus Human Assessment in the Treatment of Upper Facial Wrinkles with Abobotulinum Toxin A.Dermatology practical & conceptual · 2026Article
- Machine learning assessment of retinal blood flow links metabolic dysfunction and accelerated microvascular aging.Scientific reports · 2026Article
- Improving Face Age Prediction by Using Multiple-Angle Photos.Computational and structural biotechnology journal · 2026Article
- Artificial intelligence approaches in biological age prediction: current status and challenges.British journal of biomedical science · 2026Review
- Molecular markers, mechanisms and metrics of biological aging: a scoping review.Frontiers in aging · 2026Review
- Epigenetic ageing clocks: statistical methods and emerging computational challenges.Nature reviews. Genetics · 2025Review
- Deep learning and generative artificial intelligence in aging research and healthy longevity medicine.Aging · 2025Review
- Development and validation of deep learning- and ensemble learning-based biological ages in the NHANES study.Frontiers in aging neuroscience · 2025Article
- Advances in ocular aging: combining deep learning, imaging, and liquid biopsy biomarkers.Frontiers in medicine · 2025Review
- Review
- Age Reduction After Face-Lift Surgery in Chinese Population: An Outcome Study Using Artificial Intelligence and Objective Observer-Based Assessment.Aesthetic plastic surgery · 2024Article
- Evaluating facial dermis aging in healthy Caucasian females with LC-OCT and deep learning.Scientific reports · 2024Article
- Clinical vs. chronological skin age: exploring determinants and stratum corneum protein markers of differential skin ageing in 351 healthy women.Scientific reports · 2024Article
- State-of-the-Art in Skin Fluorescent Photography for Cosmetic and Skincare Research: From Molecular Spectra to AI Image Analysis.Life (Basel, Switzerland) · 2024Review
- Multimodal Transformers and Their Applications in Drug Target Discovery for Aging and Age-Related Diseases.The journals of gerontology. Series A, Biological sciences and medical sciences · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aging biomarkers are the qualitative and quantitative indicators of the aging processes of the human body. Estimation of biological age is important for assessing the physiological state of an organism. The advent of machine learning lead to the development of the many age predictors commonly referred to as the "aging clocks" varying in biological relevance, ease of use, cost, actionability, interpretability, and applications. Here we present and investigate a novel non-invasive class of visual photographic biomarkers of aging. We developed a simple and accurate predictor of chronological age using just the anonymized images of eye corners called the PhotoAgeClock. Deep neural networks were trained on 8414 anonymized high-resolution images of eye corners labeled with the correct chronological age. For people within the age range of 20 to 80 in a specific population, the model was able to achieve a mean absolute error of 2.3 years and 95% Pearson and Spearman correlation.
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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.