Scientists have developed an artificial intelligence tool that cab screen children for foetal alcohol spectrum disorders quickly and affordably, making it accessible to more children in remote locations worldwide.
Foetal alcohol spectrum disorders (FASDs) are a group of conditions that can occur in a person whose mother drank alcohol during pregnancy.
Problems may include an abnormal appearance, short height, low body weight, small head size, poor coordination, low intelligence, behaviour problems, and problems with hearing or seeing.
The tool 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, according to the scientists from Queen's University in Toronto, University of Southern California and Duke University in the US.
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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.
FASD is still quite difficult to diagnose -- a professional diagnosis can take a long time with the current work up taking as much as an entire day, said Laurent Itti, a professor at USC.
"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 Itti.
"The current gold standard is subjective, as it involves a battery of tests and clinical evaluation. It is also costly," he said.
Researchers felt that a screening tool might be able to reach more children who might be at risk. It is estimated that millions of children will be diagnosed with fetal alcohol spectrum disorder (FASD).
This condition, when not diagnosed early in a child's life, can give rise to secondary cognitive and behavioral disabilities.
"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," said Chen Zhang, a doctoral candidate at USC.
"The machine learning pipeline behind this gives out objective and consistent estimations in minutes," said Zhang.
While this computer vision tool is not intended to replace full diagnosis by professionals, it is intended to provide important feedback so that parents can ensure that their children are seen by professionals and receive early cognitive learning and potentially behavioral interventions.
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