A University of Buffalo student is developing an app for smartphones, tablets or computers that can track eye movement to determine, in less than a minute, if a child is showing signs of autism spectrum disorder.
“This is an ongoing study on how to analyse ASD by monitoring gaze patterns. I used the Wasserstein metric, designed the system protocol, and visual stimuli using social scenes. This is teamwork, and I learned from my advisor and graduate students in the lab,” said study principal author Kun Woo Cho.
The University of Buffalo undergraduate presented his research at the IEEE Wireless Health conference at the National Institutes of Health last month.
The study, entitled “Gaze-Wasserstein: A Quantitative Screening Approach to Autism Spectrum Disorder,” was one of the top-ranked papers at the flagship Wireless Health conference this year.
Cho has developed a prototype of the app that tracks eye movements of a child looking at pictures of social scenes — for example, those with multiple people. The eye movements of someone with ASD are often different from those of a person without autism. In the study, the app had an accuracy rating of 93.96 per cent.
“Right now it is a prototype. We have to consider if other neurological conditions are included, like ADD, how that will affect the outcome,” Cho said.
Scientists believe that early detection of autism can dramatically improve the benefits of treatment. But often the disability is not suspected until a child enters school.
“The brain continues to grow and develop after birth. The earlier the diagnosis, the better. Then we can inform families and begin therapies which will improve symptoms and outcome,” said Michelle Hartley-McAndrew, co-author of the study.
Autism spectrum disorder affects one-two people per 1,000 worldwide.