Saving sight: using AI to diagnose diabetic eye disease

Saving sight: using AI to diagnose diabetic eye disease

Researchers have used artificial intelligence to support the instant diagnosis of one of the top causes of blindness, diabetes-related eye disease, in its earliest stages.

Diabetic retinopathy is the leading cause of vision loss in adults and its impact is growing worldwide, with 191 million people set to be affected by 2030.

There are no early-stage symptoms and 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-Brazilian 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, RMIT, said the method was instantaneous and cost-effective.

“We know only half of those with diabetes have regular eye exams and one-third have never been checked,” Kumar said. 

“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.”

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.

08 January 2019

Share

fundus image of a retina A fundus image of a retina, with damaged areas highlighted by the image-processing algorithm.

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.

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 said.

“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 of fundus cameras about potential collaborations to advance the technology.

The research - with lead author Parham Khojasteh and collaborators from Federal University of Sao Carlos, Federal Institute of Sao Paolo, University of Campinas and Sao Paolo State University - is published in Computers in Biology and Medicine (January 2019, Volume 104, DOI:10.1016/j.compbiomed.2018.10.031).

Be part of the conversation about Empowering Health! Join local and international leaders from industry, research and innovation, 18-20 February at RMIT. Find out more at Engaging for Impact 2019.

Story: Gosia Kaszubska

08 January 2019

Share

  • Research
  • Science and technology
Engage

Related News

news-aroma-intensity-1220px.jpg

Confinement may affect how we smell and feel about food

New research from RMIT University found confined and isolating environments changed the way people smelled and responded emotionally to certain food aromas.

New partnership with Cisco and Grampians Health to upskill healthcare sector

RMIT’s Health Transformation Lab and College of Vocational Education are piloting a program to rapidly upskill front-line staff, broader health workforce and IT professionals in the healthcare sector to meet the industry’s emerging skills needs.

Thumbnail_RA_Awards_2024_MEDIA_WALL.jpg

Aged care digital health tool recognised again on national stage

Australia’s first electronic screening and risk prediction tool for deterioration in aged care residents has won another national prize for its positive impact on health and safety.

nuclear-medicine-1220x732.jpg

RMIT responds to critical skills shortage to support early cancer diagnosis in Queensland

RMIT University will offer Queensland’s first nuclear medicine degree, addressing critical workforce shortages and shoring up cancer services across the state.

Show more
aboriginal flag
torres strait flag

Acknowledgement of Country

RMIT University acknowledges the people of the Woi wurrung and Boon wurrung language groups of the eastern Kulin Nation on whose unceded lands we conduct the business of the University. RMIT University respectfully acknowledges their Ancestors and Elders, past and present. RMIT also acknowledges the Traditional Custodians and their Ancestors of the lands and waters across Australia where we conduct our business - Artwork 'Sentient' by Hollie Johnson, Gunaikurnai and Monero Ngarigo.

More information