New method uses AI to screen for foetal alcohol spectrum disorder

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Washington D.C. [USA]: Scientists have now developed a new tool that can screen children for foetal alcohol spectrum disorder (FASD) quickly and affordably, making it accessible to more children in remote locations worldwide.

The study was conducted by scientists from the University of Southern California (USC), Queen’s University (Ontario) and Duke University.

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/away from a target, and 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.

The technique was described in a study ‘Detection of Children/Youth With Fetal Alcohol Spectrum Disorder Through Eye Movement, Psychometric, and Neuroimaging Data,’ by Chen Zhang, Angelina Paolozza, Po-He Tseng, James N. Reynolds, Douglas P. Munoz and Laurent Itti, which appeared in Frontiers in Neurology.

According to the paper’s corresponding author, Laurent Itti, 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.

Itti and his colleagues conducted this research as they 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 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.

“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, the paper’s first author, adding, “The machine learning pipeline behind this gives out objective and consistent estimations in minutes.”

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 behavioural interventions. (Agencies)

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