Artificial intelligence, or AI, is a game-changing innovation and has many capabilities. One of those is the ability to detect signs of anxiety and depression in the speech patterns of children. According to a new study published in the Journal of Biomedical and Health Informatics, a machine-learning algorithm could be an easy way to spot anxiety and depression.
The study lead author Ellen McGinnis, a Ph.D. candidate at the University of Vermont, made an objective test to find out which children are suffering to get them the help they need as soon as possible.
Early diagnosis of various conditions can be a vital aspect of treatment. Children respond well to it when their brains are still developing, but if left untreated, they’re at an even greater chance of substance abuse and suicide later in life.
The study used a mood induction task called the Trier-Social Stress Task. This task is used to create feelings of stress and anxiety in the children to see if they suffer from these conditions. The researchers picked seventy-one kids of different ages to work on a 3-minute story and were told how they would be graded.
The judge appeared to keep a straight face throughout and provided mostly negative feedback. After 90 seconds, a judge would tell them their time was up. They designed the task in a way that’s stressful, so it helps to put them in the mindset of being judged by someone.
Another task they used was to diagnose the children using a structured clinical interview and parent questionnaire. Both these methods are well-established ways of identifying internalizing disorders in children.
The researchers next fed a Machine Learning algorithm with the statistical features of an audio recording. With the algorithm, they could accurately diagnose which type of childhood disability the child had. The algorithm was highly successful at diagnosing children.
The algorithm would identify children with an internalizing disorder with 80% accuracy, then compare it to the accuracy of the parent checklist.
The study also showed that the algorithm provides results much more quickly. Once it finishes, it takes only a few seconds for the program to provide a diagnosis.
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