Scientists have developed an artificial intelligence (AI) system 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 USA, 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 cannot reliably articulate their emotional suffering, adults need to be able to infer their mental state and recognise potential mental health problems. But factors such as lists for appointments with psychologists, insurance issues and failure to recognise the symptoms by parents all contribute to children missing out on vital treatment.
Before now, researchers have been looking for ways to use artificial intelligence (AI) 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.
At the end of the research, they found the algorithm to be successful as it identifies 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. It can also give the results much more quickly as the algorithm requires just a few seconds of processing time once the task is complete to provide a diagnosis.