Using an electroretinogram (ERG) – a diagnostic test that measures the electrical activity of the retina in response to a light stimulus – researchers have deployed AI to identify specific features to classify ASD.

Measuring retinal responses of 217 children aged between 5 and 16 years (71 with diagnosed ASD and 146 children without an ASD diagnosis), researchers found that the retina generated a different retinal response in the children with ASD as compared to those who were neurotypical.

The team also found that the strongest biomarker was achieved from a single bright flash of light to the right eye, with AI processing significantly reducing the test time. The study found that higher frequency components of the retinal signal were reduced in ASD.

Conducted with the University of Connecticut and University College London, the test could be further evaluated to see if these results could be used to screen for ASD among children aged 5 to 16 years with a high level of accuracy.

ASD is a neurodevelopmental condition characterised by difficulties in reciprocal social interactions, communication, and repetitive/restrictive behaviours.

In Australia, one in 70 people is on the autism spectrum, which is around 353,880 people. Globally, rates of autism vary, with the World Health Organization estimating the prevalence as one in every 100 children.

UniSA researcher, Dr Fernando Marmolejo-Ramos said that the test could fast-track much-needed supports for thousands of children on the spectrum.

“Early interventions and appropriate support can help children with ASD improve their quality of life, but right now, there is no simple ‘test’ for ASD which means that individuals often require lengthy psychological assessments and reports to get a diagnosis,” Dr Marmolejo-Ramos said.

“This test is much quicker. By using the RETeval electroretinogram testing unit, we can collect data, and complete a screening for autism, all within as little as 10 minutes.

“This is a massive step because it alleviates time, stress and money for parents and their children.

“Importantly, the test is non-invasive and tolerated well by children, which makes the process so much easier for all involved.”

Flinders University researcher and project lead, Dr Paul Constable said looking into the eye to understand the brain helps researchers learn about how the brain develops in people with ASD.

“It’s very exciting to begin to look at new ways of using the electroretinogram with signal analysis and machine learning to help classify ASD with greater accuracy,” Constable said.

“We still need to look at younger children and also those with other conditions such as attention deficit hyperactivity disorder to see how specific this test might be, but this is an important first step.”