Using visual attention estimation on videos for automated prediction of autism spectrum disorder and symptom severity in preschool children.

ABSTRACT: Atypical visual attention in individuals with autism spectrum disorders (ASD) has been utilised as a unique diagnosis criterion in previous research. This paper presents a novel approach to the automatic and quantitative screening of ASD as well as symptom severity prediction in preschool children. We develop a novel computational pipeline that extracts learned features from a dynamic visual stimulus to classify ASD children and predict the level of ASD-related symptoms. Experimental results demonstrate promising performance that is superior to using handcrafted features and machine learning algorithms, in terms of evaluation metrics used in diagnostic tests. Using a leave-one-out cross-validation approach, we obtained an accuracy of 94.59%, a sensitivity of 100%, a specificity of 76.47% a

SUBMITTER: de Belen RAJ 

PROVIDER: S-EPMC10861059 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature