The research, published in the Journal of Biomedical and Health Informatics, suggests a machine-learning algorithm might provide a fast and easy way of diagnosing anxiety and depression - conditions that are difficult to spot and often overlooked in young people.
“We need quick, objective tests to catch kids when they are suffering,” said study lead author Ellen McGinnis, PhD candidate at the University of Vermont in the US. “The majority of kids under eight are undiagnosed.”
Early diagnosis is critical because children respond well to treatment while their brains are developing but if left untreated, they are at greater risk of substance abuse and suicide later in life.
Researchers used an adapted version of a mood induction task called the Trier-Social Stress Task, intended to cause feelings of stress and anxiety. They picked 71 children between the ages of three and eight and asked them to tell a story to a stern judge.
The researchers then used a machine-learning algorithm to analyse features of the audio recordings of each kid’s story and relate them to the child’s diagnosis.
“The algorithm was able to identify children with a diagnosis of an internalising disorder with 80% accuracy,” said senior study author Ryan McGinnis from the University of Vermont.