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AI and Machine Learning Detects Depression in Children
Researchers have developed an AI and machine learning algorithm that can detect signs of anxiety and depression in the speech patterns of young children. They hope to one day offer a fast and easy way to identify conditions that are difficult to diagnose in young children. Research shows that when these conditions are overlooked, there is an increased risk of substance abuse and suicide later in life.
AI Fills a Need to Detect Depression in Children
It’s estimated that one in five children suffer from anxiety and depression. But since children younger than eight-years-old don’t have the vocabulary to describe their emotional suffering, adults must determine their mental state and diagnose mental health problems. Long waiting lists for appointments with mental health professionals, insurance processing, and difficulty identifying symptoms all keep children from receiving necessary treatment.
Children respond well to treatment for depression while their brains develop. But if not addressed early, serious mental health issues can persist throughout adulthood. Typically, at least a one-hour session with a trained clinician is needed to look for signs of depression. Researchers hope to fully develop an AI and machine learning solution to make the diagnosis faster and more reliable.
How AI Finds Signs of Depression in Children
Developing these machine learning algorithms begins with children being diagnosed using a structured interview and parent questionnaire. These methods have proven themselves reliable for identifying mental disorders. Then, the children are subjected to the Trier-Social Stress Task. Audio recordings are taken of the children attempting to create a three-minute story. A buzzer sounds when there are 90 seconds remaining, and again at 30 seconds while they craft their stories.
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The audio recordings of the sessions are then run through a machine learning algorithm to relate the recordings to the child’s diagnosis. The AI algorithms are impressively accurate and have uncovered three audio features indicative of mental health disorders, including low-pitched voices, higher-pitched buzzer responses, and repeatable speech inflections and content.
Ongoing Applications for AI and Mental Health
AI can be used to manage depression throughout the child’s life. Researchers are developing software equipped with machine-learning algorithms that can recognize episodes of depression and provide support by utilizing natural language processing. AI can even predict a mental health episode given the relevant data.
AI chatbots used on a smartphone can offer patients immediate counseling when needed. The data gathered from machine-learning algorithms can then be used to relay information to mental health professionals. This can help clinicians and other mental health professionals to prioritize the patients most in need of assistance. Long-term trials are expected to provide deeper understanding of the impact of AI on mental health.
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