Using AI to diagnose diabetic eye disease

Using AI to diagnose diabetic eye disease
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Researchers studying diabetic retinopathy are using artificial intelligence to support instant diagnosis of diabetic eye disease.

Diabetic retinopathy causes vision loss in adults and may even be one of the leading causes of sight loss, with its impact growing worldwide, 191 million people are set to be affected by 2030, according to Psychophysical Exams as Early Indicators of Diabetic Retinopathy. Diabetic eye disease has no early-stage symptoms and may already be advanced by the time people start losing their sight. Early diagnosis and treatment can make a dramatic difference to how much vision a patient retains.

AI detecting diabetic eye disease

A team of Australian-Brazilian researchers led by RMIT University, Australia, have developed an image-processing algorithm that can automatically detect one of the key signs of the disease, fluid on the retina, with an accuracy rate of 98%.

Lead investigator Professor Dinesh Kant Kumar explains: “We know that only half of those with diabetes have regular eye exams and one-third have never been checked.”

“But the gold standard methods of diagnosing diabetic retinopathy are invasive or expensive, and often unavailable in remote or developing parts of the world.

“Our AI-driven approach delivers results that are just as accurate as clinical scans but relies on retinal images that can be generated with ordinary optometry equipment.

“Making it quicker and cheaper to detect this incurable disease could be life changing for the millions of people who are currently undiagnosed and risk losing their sight.”

An alternative and cheap way of diagnosing diabetic eye disease

Fluorescein angiography and optical coherence tomography scans are currently the most accurate clinical methods for diagnosing diabetic retinopathy.

An alternative and cheaper method is analysing images of the retina that can be taken with relatively inexpensive equipment called fundus cameras, however the process is manual, time-consuming and less reliable.

To automate the analysis of fundus images, researchers used deep learning and artificial intelligence techniques.

The algorithm they developed can accurately and reliably spot the presence of fluid from damaged blood vessels, or exudate, inside the retina. The researchers hope their method could eventually be used for widespread screening of at-risk populations.

“Undiagnosed diabetes is a massive health problem here and around the globe,” Kumar adds.

“The ratio is one diagnosed to four undiagnosed”

Kumar explains: “For every single person in Australia who knows they have diabetes, another is living with diabetes but isn’t diagnosed. In developing countries, the ratio is one diagnosed to four undiagnosed.

“This results in millions of people developing preventable and treatable complications from diabetes-related diseases.

“With further development, our technology has the potential to reduce that burden.”
The researchers are in discussions with manufacturers about potential collaborations to advance the AI technology.

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