A team of scientists with expertise in food science and computer science/computer engineering are collaborating to develop a machine learning tool that can assist doctors and parents in the diagnosis of autism spectrum disorder (ASD) in young children.
The researchers have looked at biometric data and behavioural responses to strong smells and tastes as a way of detecting indicators of autism.
The University of Arkansas' Han-Seok Seo and Khoa Luu have created a deep-learning algorithm that could potentially identify sensory cues from various foods in both neurotypical children and those known to be on the spectrum. The AI then analyses these responses, detecting the cases in which they correlate with behaviours associated with autism.
In addition to difficulties in certain social interactions, children with ASD often exhibit abnormal eating behaviours, such as avoidance of certain foods, specific mealtime requirements and non-social eating.
Aware...