Early Autism Diagnosis Breakthrough: Machine Learning Uses Eye Movements On Real And Artificial Faces

In a recent study published in Frontiers in Neuroscience, researchers developed an innovative autism spectrum disorder (ASD) detection technique for children by examining eye movement information for cartoon choices with machine learning. The study assessed ASD and typically developing (TD) children aged 12 to 60 months using eye-tracking tests based on films of cartoon characters and actual people. Random forest (RF) classifiers were utilized for feature selection, and diagnostic data and flattened vectors were used as labels and features. Logistic regression modeling was used to analyze the impacts of the most important characteristics, and the Gesell development scale (GDS) was used to examine children’s development.