Detecting diabetic retinopathy at the earliest possible stage is crucial to protecting a person’s vision.

But the complexity of managing diabetes and access to eyecare professionals, particularly in rural settings, means the test is not performed as often as it should be.

Research led by Dr Wenyi Hu and Associate Professor Lisa Zhuoting Zhu at CERA, shows that an accurate AI-powered camera could prevent up to 40,000 cases of blindness from the disease over a 40-year period.

Their study, published in eClinicalMedicine, found that by being cheaper to perform than a traditional test and not needing specialised training, an AI scan could be incorporated into many more care settings.

“Our modelling has shown that if we’re able to screen over 80 per cent of the diabetic population to find the undetected cases of diabetic retinopathy in the community and protect their vision, we would not only save their sight but save the healthcare system many thousands of dollars in support services,” explains Dr Hu.

“There would be some increase in the cost of treating the disease, but since more people get treated earlier, fewer people will progress to blindness – which is our ultimate goal.”

The team performed this modelling by comparing an AI-powered system against conventional manual screening at estimated rates of detected and undetected diabetic retinopathy in the community.

The study supports the team’s work developing an AI-powered camera that has the capacity to detect a whole host of conditions that are visible through the eye.

“By making this test cheaper and able to be introduced into routine care for everyone, we can find and save the sight of people who might not yet know they have diabetes,” says Dr Hu.

“A universal screening program, which is accessible to as many people as possible, could mean more people are able to start treatment before they lose their vision.” 

This story was originally published in Share our vision: Annual Review 2024.