AI could be the secret to faster ASD diagnosis, South Australian researchers say
Artificial intelligence (AI) could be the key to delivering a faster and more accurate autism spectrum disorder (ASD) diagnosis, researchers from the University of South Australia (Uni SA) and Flinders University have said.
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.
“Because the eye is connected to the brain, looking into the eye to understand the brain lets us learn more about how the brain develops in people with ASD,” project lead, Flinders University researcher Dr Paul Constable said.
“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,” he added.
“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.”
Measuring retinal responses of 217 children aged 5-16 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 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.
UniSA researcher, Dr Fernando Marmolejo-Ramos says that the test could provide clinicians with an improved method for ASD diagnoses, fast-tracking much needed support 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.”
The next steps, University of Connecticut Associate Professor Dr Hugo Posada-Quintero said, will be to extend the research to look at other cohorts and diagnostic categories.
“Our study demonstrates the promising potential of analysing retinal responses using advanced signal processing and machine learning techniques to aid in the identification of neurodevelopmental conditions like autism spectrum disorder,” he added.
“With further research and technological development, these analytic methods could be developed into practical tools to help clinicians accurately and efficiently screen for and diagnose ASD and related disorders.”
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