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

• Published in: PloS one 2024-01, Vol.19 (2), p.e0282818
• Main Authors: Ryan Anthony J de Belen, Valsamma Eapen, Tomasz Bednarz, Arcot Sowmya
• Format: Article
• Language: English
• ISSN: 1932-6203
• DOI: 10.1371/journal.pone.0282818

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.