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Détail de l'auteur
Auteur Wenbo LIU |
Documents disponibles écrits par cet auteur (1)
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Identifying children with autism spectrum disorder based on their face processing abnormality: A machine learning framework / Wenbo LIU in Autism Research, 9-8 (August 2016)
[article]
Titre : Identifying children with autism spectrum disorder based on their face processing abnormality: A machine learning framework Type de document : Texte imprimé et/ou numérique Auteurs : Wenbo LIU, Auteur ; Ming LI, Auteur ; Li YI, Auteur Article en page(s) : p.888-898 Langues : Anglais (eng) Mots-clés : autism spectrum disorder face processing eye tracking machine learning Index. décimale : PER Périodiques Résumé : The atypical face scanning patterns in individuals with Autism Spectrum Disorder (ASD) has been repeatedly discovered by previous research. The present study examined whether their face scanning patterns could be potentially useful to identify children with ASD by adopting the machine learning algorithm for the classification purpose. Particularly, we applied the machine learning method to analyze an eye movement dataset from a face recognition task [Yi et al., 2016], to classify children with and without ASD. We evaluated the performance of our model in terms of its accuracy, sensitivity, and specificity of classifying ASD. Results indicated promising evidence for applying the machine learning algorithm based on the face scanning patterns to identify children with ASD, with a maximum classification accuracy of 88.51%. Nevertheless, our study is still preliminary with some constraints that may apply in the clinical practice. Future research should shed light on further valuation of our method and contribute to the development of a multitask and multimodel approach to aid the process of early detection and diagnosis of ASD. Autism Res 2016, 9: 888–898. © 2016 En ligne : http://dx.doi.org/10.1002/aur.1615 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=293
in Autism Research > 9-8 (August 2016) . - p.888-898[article] Identifying children with autism spectrum disorder based on their face processing abnormality: A machine learning framework [Texte imprimé et/ou numérique] / Wenbo LIU, Auteur ; Ming LI, Auteur ; Li YI, Auteur . - p.888-898.
Langues : Anglais (eng)
in Autism Research > 9-8 (August 2016) . - p.888-898
Mots-clés : autism spectrum disorder face processing eye tracking machine learning Index. décimale : PER Périodiques Résumé : The atypical face scanning patterns in individuals with Autism Spectrum Disorder (ASD) has been repeatedly discovered by previous research. The present study examined whether their face scanning patterns could be potentially useful to identify children with ASD by adopting the machine learning algorithm for the classification purpose. Particularly, we applied the machine learning method to analyze an eye movement dataset from a face recognition task [Yi et al., 2016], to classify children with and without ASD. We evaluated the performance of our model in terms of its accuracy, sensitivity, and specificity of classifying ASD. Results indicated promising evidence for applying the machine learning algorithm based on the face scanning patterns to identify children with ASD, with a maximum classification accuracy of 88.51%. Nevertheless, our study is still preliminary with some constraints that may apply in the clinical practice. Future research should shed light on further valuation of our method and contribute to the development of a multitask and multimodel approach to aid the process of early detection and diagnosis of ASD. Autism Res 2016, 9: 888–898. © 2016 En ligne : http://dx.doi.org/10.1002/aur.1615 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=293