Algorithm predicts autism diagnosis in young children with 81 percent accuracy

An algorithm that's able to accurately predict autism diagnoses in young kids could enable potental interventions to be made earlier. A team of researchers at the University of North Carolina at Chapel Hill have developed a deep learning algorithm that can accurately predict whether a child at high risk of autism is likely to be diagnosed with the disorder in early childhood. The algorithm was able to predict with 81 percent accuracy whether a diagnosis of autism would be made for a child with an autistic sibling,. The deep learning tool was developed in conjunction with computer scientists from the College of Charleston as part of the Infant Brain Imaging Study, which focuses on early brain development among children with autism. By scanning their brains at 6 months old, a year old, and 2 years old, they were able to make some interesting discoveries.