[article] 
inAutism Research > 14-3 (March 2021) . - p.488-499

Titre :	Digital Behavioral Phenotyping Detects Atypical Pattern of Facial Expression in Toddlers with Autism
Type de document :	Texte imprimé et/ou numérique
Auteurs :	Kimberly L. H. CARPENTER, Auteur ; Jordan HAHEMI, Auteur ; Kathleen CAMPBELL, Auteur ; Steven J. LIPPMANN, Auteur ; Jeffrey P. BAKER, Auteur ; Helen L. EGGER, Auteur ; Steven ESPINOSA, Auteur ; Saritha VERMEER, Auteur ; Guillermo SAPIRO, Auteur ; Geraldine DAWSON, Auteur
Article en page(s) :	p.488-499
Langues :	Anglais (eng)
Mots-clés :	autism  computer vision  early detection  facial expressions  risk behaviors  Amazon, Google, Cisco, and Microsoft and is a consultant for Apple and Volvo.  Geraldine Dawson is on the Scientific Advisory Boards of Janssen Research and  Development, Akili, Inc., LabCorp, Inc., Tris Pharma, and Roche Pharmaceutical  Company, a consultant for Apple, Inc, Gerson Lehrman Group, Guidepoint, Inc., Teva  Pharmaceuticals, and Axial Ventures, has received grant funding from Janssen  Research and Development, and is CEO of DASIO, LLC (with Guillermo Sapiro). Dawson  receives royalties from Guilford Press, Springer, and Oxford University Press.  Dawson, Sapiro, Carpenter, Hashemi, Campbell, Espinosa, Baker, and Egger helped  develop aspects of the technology that is being used in the study. The technology  has been licensed and Dawson, Sapiro, Carpenter, Hashemi, Espinosa, Baker, Egger,  and Duke University have benefited financially.
Index. décimale :	PER Périodiques
Résumé :	Commonly used screening tools for autism spectrum disorder (ASD) generally rely on subjective caregiver questionnaires. While behavioral observation is more objective, it is also expensive, time-consuming, and requires significant expertise to perform. As such, there remains a critical need to develop feasible, scalable, and reliable tools that can characterize ASD risk behaviors. This study assessed the utility of a tablet-based behavioral assessment for eliciting and detecting one type of risk behavior, namely, patterns of facial expression, in 104 toddlers (ASD N =?22) and evaluated whether such patterns differentiated toddlers with and without ASD. The assessment consisted of the child sitting on his/her caregiver's lap and watching brief movies shown on a smart tablet while the embedded camera recorded the child's facial expressions. Computer vision analysis (CVA) automatically detected and tracked facial landmarks, which were used to estimate head position and facial expressions (Positive, Neutral, All Other). Using CVA, specific points throughout the movies were identified that reliably differentiate between children with and without ASD based on their patterns of facial movement and expressions (area under the curves for individual movies ranging from 0.62 to 0.73). During these instances, children with ASD more frequently displayed Neutral expressions compared to children without ASD, who had more All Other expressions. The frequency of All Other expressions was driven by non-ASD children more often displaying raised eyebrows and an open mouth, characteristic of engagement/interest. Preliminary results suggest computational coding of facial movements and expressions via a tablet-based assessment can detect differences in affective expression, one of the early, core features of ASD. LAY SUMMARY: This study tested the use of a tablet in the behavioral assessment of young children with autism. Children watched a series of developmentally appropriate movies and their facial expressions were recorded using the camera embedded in the tablet. Results suggest that computational assessments of facial expressions may be useful in early detection of symptoms of autism.
En ligne :	http://dx.doi.org/10.1002/aur.2391
Permalink :	https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=443

[article] Digital Behavioral Phenotyping Detects Atypical Pattern of Facial Expression in Toddlers with Autism [Texte imprimé et/ou numérique] / Kimberly L. H. CARPENTER, Auteur ; Jordan HAHEMI, Auteur ; Kathleen CAMPBELL, Auteur ; Steven J. LIPPMANN, Auteur ; Jeffrey P. BAKER, Auteur ; Helen L. EGGER, Auteur ; Steven ESPINOSA, Auteur ; Saritha VERMEER, Auteur ; Guillermo SAPIRO, Auteur ; Geraldine DAWSON, Auteur . - p.488-499.
Langues : Anglais (eng)
in Autism Research > 14-3 (March 2021) . - p.488-499

Mots-clés :	autism  computer vision  early detection  facial expressions  risk behaviors  Amazon, Google, Cisco, and Microsoft and is a consultant for Apple and Volvo.  Geraldine Dawson is on the Scientific Advisory Boards of Janssen Research and  Development, Akili, Inc., LabCorp, Inc., Tris Pharma, and Roche Pharmaceutical  Company, a consultant for Apple, Inc, Gerson Lehrman Group, Guidepoint, Inc., Teva  Pharmaceuticals, and Axial Ventures, has received grant funding from Janssen  Research and Development, and is CEO of DASIO, LLC (with Guillermo Sapiro). Dawson  receives royalties from Guilford Press, Springer, and Oxford University Press.  Dawson, Sapiro, Carpenter, Hashemi, Campbell, Espinosa, Baker, and Egger helped  develop aspects of the technology that is being used in the study. The technology  has been licensed and Dawson, Sapiro, Carpenter, Hashemi, Espinosa, Baker, Egger,  and Duke University have benefited financially.
Index. décimale :	PER Périodiques
Résumé :	Commonly used screening tools for autism spectrum disorder (ASD) generally rely on subjective caregiver questionnaires. While behavioral observation is more objective, it is also expensive, time-consuming, and requires significant expertise to perform. As such, there remains a critical need to develop feasible, scalable, and reliable tools that can characterize ASD risk behaviors. This study assessed the utility of a tablet-based behavioral assessment for eliciting and detecting one type of risk behavior, namely, patterns of facial expression, in 104 toddlers (ASD N =?22) and evaluated whether such patterns differentiated toddlers with and without ASD. The assessment consisted of the child sitting on his/her caregiver's lap and watching brief movies shown on a smart tablet while the embedded camera recorded the child's facial expressions. Computer vision analysis (CVA) automatically detected and tracked facial landmarks, which were used to estimate head position and facial expressions (Positive, Neutral, All Other). Using CVA, specific points throughout the movies were identified that reliably differentiate between children with and without ASD based on their patterns of facial movement and expressions (area under the curves for individual movies ranging from 0.62 to 0.73). During these instances, children with ASD more frequently displayed Neutral expressions compared to children without ASD, who had more All Other expressions. The frequency of All Other expressions was driven by non-ASD children more often displaying raised eyebrows and an open mouth, characteristic of engagement/interest. Preliminary results suggest computational coding of facial movements and expressions via a tablet-based assessment can detect differences in affective expression, one of the early, core features of ASD. LAY SUMMARY: This study tested the use of a tablet in the behavioral assessment of young children with autism. Children watched a series of developmentally appropriate movies and their facial expressions were recorded using the camera embedded in the tablet. Results suggest that computational assessments of facial expressions may be useful in early detection of symptoms of autism.
En ligne :	http://dx.doi.org/10.1002/aur.2391
Permalink :	https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=443