Deep Learning on Natural Viewing Behaviors to Differentiate Children with Fetal Alcohol Spectrum Disorder

Computational models of visual attention have attracted strong interest by accurately predicting how humans deploy attention. However, little research has utilized these models to detect clinical populations whose attention control has been affected by neurological disorders. We designed a framework to decypher disorders from the joint analysis of video and patients' natural eye movement behaviors watch television for 5 minutes. We employ convolutional deep neural networks to extract visual features in real-time at the point of gaze, followed by SVM and Adaboost to classify typically developing children vs. children with fetal alcohol spectrum disorder FASD, who exhibit impaired attentional control. The classifier achieved 74.1% accuracy ROC: 0.82. Our results demonstrate that there is substantial information about attentional control in even very short recordings of natural viewing behavior. Our new method could lead to high-throughput, low-cost screening tools for identifying individuals with deficits in attentional control.