Machine Learning And Artificial Intelligence Can Be Used To Predict Autism In Children – AI Next
Early diagnosis of autism is crucial for effective early interventions, but determining which children may develop autism can be challenging. Researchers have made significant progress in understanding the disorder, but since autism spectrum disorder (ASD) is behavior-based, predicting its development is still difficult.
ASD is a neurodevelopmental condition characterized by behavioral patterns and social skill deficits. Currently, diagnosis typically occurs around the age of two. Diagnostic techniques, such as the Autism Diagnostic Observation Schedule (ADOS), involve observing a child’s behavior through videotapes.
Boston Children’s Hospital has utilized Electroencephalography to identify brain functions related to autism development. Recent studies by researchers from the University of North Carolina and Washington University have shown a 96 percent accuracy rate in predicting autism in children under 24 months old.
By mapping brain areas from high-risk infants’ MRI scans, researchers developed a machine learning algorithm that could predict autism with 81.8% sensitivity. The study found that 9 out of 11 infants diagnosed with autism at 24 months were accurately identified by the algorithm.
This breakthrough highlights the potential of AI and machine learning in diagnosing and treating neurological disorders early on. The data-driven approach could revolutionize not only the early detection of autism but also the prediction of its severity.
By leveraging advanced technology and neural imaging, researchers aim to develop more accurate and reliable methods for diagnosing and treating autism spectrum disorder. The use of artificial intelligence holds promise in transforming the field of pediatric neurology and improving outcomes for children with developmental disorders.
