Artificial intelligence is revolutionizing the diagnosis of neurodivergent disorders like autism and ADHD. Researchers at Indiana University have developed a diagnostic approach using AI that can speed up and improve the detection of these conditions. The approach uses deep learning to analyze raw movement data from participants, allowing for a more objective and accurate assessment of neurodivergent disorders.
In a study, participants were instructed to reach for a target on a computer touch screen while sensors recorded hundreds of images of micromovements per second. The AI algorithm analyzed the movement data to identify patterns and correlations between movement randomness and the severity of neurodivergent disorders. This approach enables researchers to assess the severity of a disorder and provide a more accurate diagnosis.
The AI-powered method can diagnose autism or ADHD in as little as 15 minutes, significantly reducing the current wait time of up to 18 months for a diagnostic appointment with a psychiatrist in Indiana. This rapid diagnosis can help identify individuals who need quick intervention, enabling them to receive targeted support and treatment earlier.
By providing a more accurate and objective assessment of neurodivergent disorders, the approach can help healthcare providers develop more effective treatment plans. The researchers believe that this technology has the potential to improve the diagnosis and treatment of neurodivergent disorders, ultimately enhancing the lives of individuals affected by these conditions.
