Pilot Study for Early Screening of Autism Spectrum Disorder Using Baby Cry

We developed an algorithm for the early screening of autism spectrum disorder using baby cry sound. We collected the sound using a smartphone application to record it in their daily life. We employed the Japanese version of the Modified Checklist for Autism in Toddlers (M-CHAT) for the evaluation of the autism spectrum. We built a prediction model for binary classification using least absolute shrinkage and selection operator (LASSO) logistic regression. We evaluated the validity of the features as explanatory variables used in the model by cross-validation, and the accuracy of the file-wise prediction in the cross-validation was 0.63. This result proved the feasibility of the very early-stage screening of autism spectrum using baby cry.