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Diabetic Retinopathy

Diabetic Retinopathy

DefinitionDiabetic retinopathy is a microvascular complication of diabetes affecting the retinal blood vessels.Risk FactorsDiabetic retinopathy is present in almost all individuals with type 1 diabetes and more than 60% of those with type 2 diabetes after two decades of disease.The risk increases with poor glycemic control, hypertension, dyslipidemia, smoking, pregnancy, and puberty.Although cataracts and glaucoma are also more frequent in people with diabetes, retinopathy remains the leading...

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Factors Associated with Machine Learning-Based Predictions of Retinal Aging Using Teleretinal Screening Images from

Tuyet Thao Nguyen¹, Maria Jessica Cruz¹, Tanvi Chokshi¹

¹Department of Ophthalmology & Vision Science, University of California Davis, Sacramento, California.

Ophthalmology Science

|March 19, 2026

Summary

Machine learning accurately predicts chronological age from retinal images of patients with diabetes. The retinal age gap is linked to cardiovascular disease risk and accelerated by comorbidities like smoking and hypertension.

Keywords:

Cardiovascular risk Diabetes Retinal age gap Teleophthalmology Teleretinal screening

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Area of Science:

Ophthalmology
Artificial Intelligence
Cardiovascular Medicine

Background:

Teleretinal screening is crucial for managing diabetes complications.
Predicting biological aging from retinal images offers insights into systemic health.
Machine learning models can analyze fundus photographs for age prediction.

Purpose of the Study:

To identify factors associated with accelerated retinal aging.
To predict chronological age using machine learning on fundus images from patients with diabetes.
To analyze the association between the retinal age gap and demographic, lifestyle, and systemic health factors.

Main Methods:

A vision transformer (ViT) model was trained to predict chronological age from retinal fundus photographs.
The model was validated using images from teleretinal screening data and an external dataset.
Demographic, lifestyle, and systemic health factors were collected and analyzed for association with the retinal age gap.

Main Results:

The ViT model accurately predicted chronological age (mean absolute error 4.43 years; R² = 0.84).
The retinal age gap correlated with predicted 10-year cardiovascular disease risk (heart failure, stroke).
Accelerated retinal aging was associated with active smoking, severe obesity, hypertension, hyperlipidemia, and diabetic neuropathy. Retinal aging was lower in Black patients.

Conclusions:

Machine learning predictions of retinal aging from teleretinal images may serve as a biomarker for cardiovascular risk.
Systemic comorbidities significantly influence the rate of retinal aging in patients with diabetes.
Further research is needed to explore the clinical utility of retinal age gap prediction.