A machine learning algorithm can detect signs of anxiety and depression in the speech patterns of young children, potentially providing a fast and easy way of diagnosing conditions that are difficult to spot and often overlooked in young people, according to new research published in the Journal of Biomedical and Health Informatics.
Around one in five children suffer from anxiety and depression, collectively known as “internalizing disorders.” But because children under the age of eight can’t reliably articulate their emotional suffering, adults need to be able to infer their mental state, and recognise potential mental health problems. Waiting lists for appointments with psychologists, insurance issues, and failure to recognise the symptoms by parents all contribute to children missing out on vital treatment.
“We need quick, objective tests to catch kids when they are suffering,” says Ellen McGinnis, a clinical psychologist at the University of Vermont Medical Center’s Vermont Center for Children, Youth and Families and lead author of the study. “The majority of kids under eight are undiagnosed.”
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. McGinnis, along with University of Vermont biomedical engineer and study co-author Ryan McGinnis, has been looking for ways to use artificial intelligence and machine learning to make diagnosis faster and more reliable.
“The algorithm was able to identify children with a diagnosis of an internalizing disorder with 80% accuracy, and in most cases that compared really well to the accuracy of the parent checklist,” says Ryan McGinnis. 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.
The algorithm identified eight different audio features of the children’s speech, but three in particular stood out as highly indicative of internalizing disorders: low-pitched voices, with repeatable speech inflections and content, and a higher-pitched response to the surprising buzzer. Ellen McGinnis says these features fit well with what you might expect from someone suffering from depression. “A low-pitched voice and repeatable speech elements mirror what we think about when we think about depression: speaking in a monotone voice, repeating what you’re saying,” says Ellen McGinnis.