An artificial intelligence (AI) system developed that can detect signs of anxiety and depression in the speech patterns of young children. 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.”
Recognized the symptoms by parents all contribute to children missing vital treatment.
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 gave only neutral or negative feedback.
The children were also diagnosed using a structured clinical interview and parent questionnaire, both well-established ways of identifying internalizing 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 internalizing 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.