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According to the research published in the Journal of Biomedical and Health Informatics, the tool potentially provides a fast and easy way of diagnosing conditions that are difficult to spot and often overlooked in young people.
Around one in five children suffer from anxiety and depression, collectively known as “internalising disorders.”
However, since children under the age of eight can not reliably articulate their emotional suffering, adults need to be able to infer their mental state, and recognise potential mental health problems.
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“We need quick, objective tests to catch kids when they are suffering,” said Ellen McGinnis, a clinical psychologist at the University of Vermont in the US.
“The majority of kids under eight are undiagnosed,” said McGinnis, lead author of the study.
Early diagnosis is critical because children respond well to treatment while their brains are still developing, but if they are left untreated they are at greater risk of substance abuse and suicide later in life.
Standard diagnosis involves a 60-90 minute semi-structured interview with a trained clinician and their primary care-giver.
Researchers have been looking for ways to use artificial intelligence and machine learning to make diagnosis faster and more reliable.
They used an adapted version of a mood induction task called the Trier-Social Stress Task, which is intended to cause feelings of stress and anxiety in the subject.
A group of 71 children between the ages of three and eight were asked to improvise a three-minute story, and told that they would be judged based on how interesting it was.
The researcher acting as the judge remained stern throughout the speech, and gave only neutral or negative feedback.
After 90 seconds, and again with 30 seconds left, a buzzer would sound and the judge would tell them how much time was left.
“The task is designed to be stressful, and to put them in the mindset that someone was judging them,” said McGinnis.
The children were also diagnosed using a structured clinical interview and parent questionnaire, both well-established ways of identifying internalising disorders in children.
The researchers used a machine learning algorithm to analyse statistical features of the audio recordings of each kid’s story and relate them to the child’s diagnosis.
They found the algorithm was highly successful at diagnosing children, and that the middle phase of the recordings, between the two buzzers, was the most predictive of a diagnosis.
The algorithm was able to identify children with a diagnosis of an internalising disorder with 80 per cent accuracy, and in most cases that compared really well to the accuracy of the parent checklist, researchers said.
It can also give the results much more quickly — the algorithm requires just a few seconds of processing time once the task is complete to provide a diagnosis.
McGinnis said that the next step will be to develop the speech analysis algorithm into a universal screening tool for clinical use, perhaps via a smartphone app that could record and analyse results immediately.
The voice analysis could also be combined with the motion analysis into a battery of technology-assisted diagnostic tools, to help identify children at risk of anxiety and depression before even their parents suspect that anything is wrong.