Machine Learning tool to identify kids with alcohol-related developmental disorder

Researchers have developed a new tool that uses Machine Learning (ML) technology to screen children suffering from a type of developmental and neurobehavioural disorder, caused due to alcohol exposure while in the womb, quickly and at an affordable rate.

It is estimated that millions of children will be diagnosed with foetal alcohol spectrum disorder (FASD). This condition, when not diagnosed early in a child's life, can give rise to secondary cognitive and behavioural disabilities.

FASD is still quite difficult to diagnose. A professional diagnosis can take a long time with the current work taking as much as an entire day. But, the ML tool, developed by researchers from the University of Southern California (USC) in the US, uses a camera and computer vision to record patterns in children's eye movements as they watch multiple one-minute videos, or look towards or away from a target.

It then identifies patterns that contrast to recorded eye movements by other children who watched the same videos or targets. The eye movements outside the norm were flagged by the researchers as children who might be at risk for having FASD and need more formal diagnoses by healthcare practitioners, according to study published in Frontiers in Neurology journal.

"There is not a simple blood test to diagnose FASD. It is one of those spectrum disorders where there is a broad range of the disorder. It is medically very challenging and it is co-morbid with other conditions," said Laurent Itti, Professor at the USC. 

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"The new screening procedure only involves a camera and a computer screen, and can be applied to very young children. It takes only 10 to 20 minutes and the cost should be affordable in most cases," added Chen Zhang from the varsity. 

While this computer vision tool is not intended to replace full diagnosis, it could provide important feedback so that parents can ensure that their children are seen by professionals and receive early cognitive learning and potentially behavioural interventions, the researchers noted.