A team from the Department of Computational Biology at the University of Lausanne has developed an artificial intelligence model capable of estimating a person's retinal age from a simple photograph of the back of the eye. This measure provides information about health and future risk of developing age-associated diseases.
Our retina contains far more than just information about our eyes. Its blood vessels are directly visible, it is affected by conditions like diabetes and inflammation, and as part of the central nervous system, it is closely connected to the brain. A simple photograph of the retina can therefore provide a remarkable window into our health.
Olga Trofimova, a postdoctoral researcher in Professor Sven Bergmann's research group in the Department of Computational Biology (DBC) at the University of Lausanne, used an artificial intelligence (AI) model to ask a seemingly simple question: how old is a person's retina? The difference between the AI-estimated age and the person's actual age provides insights into biological ageing, helping to identify those with accelerated ageing and a higher risk of certain diseases.
The study, carried out in collaboration with colleagues at the Jules-Gonin Eye Hospital in Lausanne and Erasmus University Medical Center in Rotterdam, is published in the 26 August 2026 issue of Nature Communications.
The gap between retinal age and chronological age provides important clues
The researchers adapted RETFound, an AI foundation model specialised in retinal image analysis, training it on images from more than 70,000 UK Biobank study participants aged 40 to 79. The model learned to estimate chronological age from a retinal image, with an average error of less than three years.
The researchers then examined the difference between this estimated retinal age and a person's actual age. "The difference between these two ages, called the retinal age gap, is linked to many aspects of health: a higher retinal age is associated with an increased risk of cardiovascular and respiratory diseases, cancer, dementia and death over the following fifteen years. It is also associated with markers of metabolism, inflammation, cognitive abilities and lifestyle," explains Olga Trofimova, first author of the paper, who is also affiliated with the Swiss Institute of Bioinformatics ( SIB ).
Sex-specific patterns and a shift around menopause
More surprisingly, the biological signals associated with retinal ageing differ between men and women and change in women around menopause.
As the study authors explain, in men, a retina that appears older is more strongly associated with features of metabolic syndrome, which include high blood pressure, diabetes, high cholesterol and being overweight. In women, retinal ageing is more closely linked to vascular factors. For example, a higher retinal age is associated with an increased risk of thrombosis, an association that was not found in men.
While the results differ according to sex, they also vary with age. "Before menopause, women on average have retinas that appear younger than those of men. After menopause, the pattern is reversed, with a higher retinal age and a less favourable health profile," Olga Trofimova explains. "The study cannot establish that menopause itself causes these changes, but it points to a possible role of the hormonal and vascular changes that accompany it."
What does the AI see?
But what exactly does AI see that we cannot? The full answer is still not known, although the researchers have been able to study which parts of the image are important to the model. "The human eye does not perceive the same things as AI. The model tends to focus on certain areas of the retina and detect subtle variations in colour or brightness, as well as features that are difficult to perceive with the naked eye, such as blood vessel density. This link with vascularisation appears to be particularly strong in women," she notes.
The model is large and complex, but it is not entirely a black box. "Some of the features taken into account by the model remain difficult to interpret, but the value of these models also lies in their ability to reveal information about health that we may not yet be able to explain in detail," she comments. Understanding precisely how these models reach their conclusions is an important area of research.
"Transformers are one of the technologies behind the spectacular advances in language models such as ChatGPT. The same principle has also transformed computer vision," adds Sven Bergmann, Professor at the University of Lausanne and senior author of the study. "Combined with so-called foundation models, trained on very large collections of biomedical images, they allow us to extract information from retinal images that would have been extremely difficult to access just a few years ago."
A biomarker, not a diagnosis
The scientists nevertheless stress that retinal age is not a diagnosis. A retina that appears older than expected does not mean that a person has, or will develop, a particular disease. "We are talking primarily about risk," Olga Trofimova emphasises. "The retinal age gap is a potential prognostic marker, not a diagnostic test."
However, the links observed with lifestyle and cardiometabolic health point to potential avenues for action. Although the study cannot establish causal relationships, some of the factors involved may be modifiable, particularly lifestyle habits. "Smoking was the factor most strongly associated with retinal ageing, while early management of cardiometabolic problems, such as high blood pressure, could also play a role," Olga Trofimova adds.
Bringing it into the clinic will take time
Taking a photograph of a person's retina is a simple, accessible, inexpensive and minimally invasive method that can provide a wealth of information. Retinal imaging is therefore a potentially valuable source of biomarkers that could one day complement conventional measures of health and ageing. Its application in clinical practice will nevertheless take time. "We are still at the research stage. Further studies will be needed to determine to what extent the findings can be generalised to more diverse populations and whether, in the long term, this approach can provide useful information for clinical decision-making," the researcher concludes.
