How AI-based eye scans can detect high blood sugar, heart disease

The integration of machine learning and artificial intelligence (AI) is providing a new lens for viewing retinal health, transforming the field of ophthalmology from a reactive one to proactive and predictive.

Retinal images offer a non-invasive view of blood vessels and nerve fibres. They are not just a window into the eye, but also a valuable diagnostic tool for a host of diseases.

For instance, the narrowing of small blood vessels in the retina, called retinal arterioles, is related to long-term risk of high blood pressure, while a larger diameter or width of retinal veins is related to kidney issues in people with Type-1 diabetes.

Moreover, arteriolar-to-venular diameter ratio is an established biomarker for stroke and heart diseases.

Retinal vessels

The retina thus provides a unique opportunity to assess and diagnose various ailments such as diabetes mellitus, coronary heart disease, high blood pressure, kidney disease, and neurodegenerative disorders.

This is because the structure of retinal vessels can be considered a witness of the patient’s vascular status.

With an increase in the aging population and poor lifestyle choices, the prevalence of these diseases is on the rise. Early diagnosis and identifying high-risk individuals is the need of the hour.

The last two decades have witnessed a growing interest in imaging of the blood vessels of the retina. Technology to capture retinal images, such as retinal fundus photography, optical coherence tomography–angiography (OCT-A), or adaptive optics, have made it possible to get accurate data on our circulatory system.

Fundus photography

Fundus photography is used to capture images of the inside of the eye, which includes structures such as the retina, optic nerve head, macula, retinal blood vessels, choroid and the vitreous.

These images are used to screen and detect various causes of treatable and preventable blindness, such as diabetic retinopathy, age-related macular degeneration, and glaucoma, among others.

OCT-A is used to obtain detailed visuals of the vascular networks of the retina; it is non-invasive, time-efficient, and allows for a three-dimensional examination of the retina.

Over the last decade, much research has been going on to develop software that can enable an automatic analysis of the retinal vascular network from these imaging techniques to provide an accurate description of the patient’s arteries and veins.

Recently, a new approach called “oculomics”, which uses retinal image datasets and AI algorithms, has increased interest in retinal microvascular biomarkers.

Generative AI and eye surgery

A common problem that AI can help resolve in the field of ophthalmology is improving surgical outcomes for patients with macular holes, a condition that causes central vision loss.

Macular holes are defects in the macula, a part of the retina. Those who have the disease have problems seeing clearly, especially in their central field of vision.

Surgery to treat a macular hole, called vitrectomy, has high success rates if the hole is small.

Despite being the standard treatment for the disease, the success of the surgery can vary — a failed macular hole surgery often requires another attempt, increased expenditure and emotional stress for the patient.

Predictive capability

Here, AI tools that can learn from pre- and post-operative images can be leveraged. The technology can help predict what a patient’s retina will look like after surgery, including the likelihood of the macular hole closing.

This predictive capability is a significant leap forward, providing a powerful tool for surgeons to plan the procedure appropriately and counsel patients before surgery, helping them make more informed decisions and setting accurate expectations.

Non-invasive screening for diabetes

A second, equally impactful project is motivated by the need for more accessible and non-invasive diagnostic tools for diabetes.

Current screening methods for glycated haemoglobin (HbA1c) levels — the test measures average blood sugar levels over the past 90 days, expressed as a percentage — typically require blood samples, which can be inconvenient and create barriers to care.

This is a particularly critical issue for India, which is now considered the diabetes capital of the world.

As per the International Diabetes Federation (IDF) Atlas 11th edition, India surpassed China with the highest number of diabetic individuals globally, and the number is predicted to increase by 75 percent in the next 25 years.

This highlights the urgent need for a scalable, cost-effective solution that obviates the need for a blood test. Researchers on this project are developing a deep learning framework that can classify HbA1c levels directly from retinal images.

User-friendly

The designed model is highly robust and accurate — the programme has learned to identify patterns in eye images that are connected to a person’s average blood sugar level (HbA1c).

Depending on the patterns, it can give a simple “Yes/No” answer on whether blood sugar is in a healthy range. It can also provide a more detailed report that classifies the levels as optimal, elevated, or high risk.

The technology can be deployed as a user-friendly application, which can be used for mass screening, making it more cost-effective than traditional blood tests for the country’s large diabetic population.

This approach could transform routine diabetes screening, allowing for earlier detection and intervention, without the need for traditional blood tests.