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

Ryan Anthony J de Belen, Valsamma Eapen, Tomasz Bednarz and Arcot Sowmya

PLOS ONE, 2024, vol. 19, issue 2, 1-33

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% and an area under the receiver operating characteristic curve (AUC) of 96% for ASD classification. In addition, we obtained an accuracy of 94.74%, a sensitivity of 87.50%, a specificity of 100% and an AUC of 99% for ASD symptom severity prediction.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0282818

DOI: 10.1371/journal.pone.0282818

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