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Artificial intelligence is being used to support the instant diagnosis of diabetic retinopathy, one of the top causes of blindness, in its earliest stages.

The diabetic-related eye disease聽is the and its impact is growing worldwide, with .

With no early-stage symptoms,聽the disease 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.

Now a team of Australian鈥揃razilian researchers led by RMIT University 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, from RMIT, said the method was instantaneous and cost-effective.

鈥淲e know that only half of those with diabetes have regular eye exams and聽one-third have never been checked,鈥� Prof Kumar said.聽鈥淏ut the gold standard methods of diagnosing diabetic retinopathy are invasive or expensive, and often unavailable in remote or developing parts of the world.

鈥淥ur 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.

鈥淢aking 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.鈥�

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, but the process is manual, time-consuming and less reliable.

To automate the analysis of fundus images, researchers in the Biosignals Laboratory in the School of Engineering at RMIT, together with collaborators in Brazil, used deep learning and artificial intelligence techniques.聽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.

鈥淯ndiagnosed diabetes is a massive health problem here and around the globe,鈥� Prof Kumar said.

鈥淔or every single person in Australia who knows they have diabetes, another is living with diabetes but isn鈥檛 diagnosed. In developing countries, the ratio is one diagnosed to four undiagnosed.

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

鈥淲ith further development, our technology has the potential to reduce that burden.鈥�

The researchers are in discussion with manufacturers of fundus cameras about potential collaborations to advance the technology.

Image credit: 漏stock.adobe.com/au/Gajus