Results of clinical studies published simultaneously in the Journal of the American Medical Association (JAMA) and in JAMA Network Open demonstrate that measuring children’s looking behaviour predicts expert clinical diagnosis of autism in about 1,500 children between ages 16 to 30 months tested with a high degree of accuracy.
Researchers from Marcus Autism Center, a subsidiary of Children’s Healthcare of Atlanta, US, have also developed a technology to reliably measure how children with autism look at and learn from their surrounding social environment.
In addition, the technology measures each child’s individual levels of social disability, verbal ability and non-verbal learning ability, which is critical information for clinicians when developing personalised treatment plans to help each child make the greatest gains.
“The results show that the way in which young children look at social information can serve as an effective and objective biomarker for early signs of autism,” said lead author Warren Jones, Director of Research at Marcus Autism Center at Children’s Healthcare of Atlanta.
“The far-reaching implications of these results may mean that children who currently have limited access to expert care, and face two or more years of waiting and referrals before finally being diagnosed at age four or five, may now be eligible for diagnosis between the ages of 16 and 30 months,” added co-author Ami Klin, Director of the Marcus Center Klin, who is also Division Chief of Autism and Developmental Disabilities at Emory University.
As per the World Health Organization (WHO) about 1 in 100 children worldwide has autism. Early identification and early intervention are important for supporting the health, learning, and long-term well-being of all children with autism.
During testing, children watched video scenes of social interaction. As they watched, their eye movements were monitored at a rate of 120 times per second to determine, moment-by-moment, what social information the children looked at and what they did not.
After collection, tens of thousands of these measurements were compared to data from age-matched peers, using algorithms to quantify similarities and differences at each moment in time.
These measurements were summarised to provide an overall diagnostic indication as well as individual measurements of each child’s levels of social disability, verbal ability, and non-verbal learning skills.
When compared with expert clinical diagnosis of autism, automated measurements of children’s looking behaviour accurately matched the current gold standard, which could help pave the way for earlier, objective diagnosis in many children, the researchers said.