Computer vision analysis captures atypical attention in toddlers with autism.
To demonstrate the capability of computer vision analysis to detect atypical orienting and attention behaviors in toddlers with autism spectrum disorder. One hundered and four toddlers of 16–31 months old (mean = 22) participated in this study. Twenty-two of the toddlers had autism spectrum disorder...
| Publicado en: | Autism: The International Journal of Research & Practice Vol. 23; no. 3; pp. 619 - 629 |
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| Autores principales: | , , , , , , , , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
Apr2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=135864070&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 135864070 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13623613 F9D jtl: Autism: The International Journal of Research & Practice issn: 13623613 maglogo: Y pubinfo: dt: Apr2019 vid: 23 iid: 3 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 135864070 135864070 135864070 10.1177/1362361318766247 135864070 ppf: 619 ppct: 10 formats: tig: atl: Computer vision analysis captures atypical attention in toddlers with autism. aug: au: Campbell, Kathleen Carpenter, Kimberly LH Hashemi, Jordan Espinosa, Steven Marsan, Samuel Borg, Jana Schaich Chang, Zhuoqing Qiu, Qiang Vermeer, Saritha Adler, Elizabeth Tepper, Mariano Egger, Helen L Baker, Jeffery P Sapiro, Guillermo Dawson, Geraldine affil: Duke University, USA sug: subj: Artificial Intelligence Utilization Attention Evaluation Orientation Evaluation Children with Disabilities Psychosocial Factors Autism Spectrum Disorder Human Infant Child, Preschool Developmental Disabilities Videorecording Head Physiology Movement Task Performance and Analysis Reliability Algorithms Intraclass Correlation Coefficient Confidence Intervals Sensitivity and Specificity Automation Infant: 1-23 months Child, Preschool: 2-5 years ab: To demonstrate the capability of computer vision analysis to detect atypical orienting and attention behaviors in toddlers with autism spectrum disorder. One hundered and four toddlers of 16–31 months old (mean = 22) participated in this study. Twenty-two of the toddlers had autism spectrum disorder and 82 had typical development or developmental delay. Toddlers watched video stimuli on a tablet while the built-in camera recorded their head movement. Computer vision analysis measured participants' attention and orienting in response to name calls. Reliability of the computer vision analysis algorithm was tested against a human rater. Differences in behavior were analyzed between the autism spectrum disorder group and the comparison group. Reliability between computer vision analysis and human coding for orienting to name was excellent (intra-class coefficient 0.84, 95% confidence interval 0.67–0.91). Only 8% of toddlers with autism spectrum disorder oriented to name calling on >1 trial, compared to 63% of toddlers in the comparison group (p = 0.002). Mean latency to orient was significantly longer for toddlers with autism spectrum disorder (2.02 vs 1.06 s, p = 0.04). Sensitivity for autism spectrum disorder of atypical orienting was 96% and specificity was 38%. Older toddlers with autism spectrum disorder showed less attention to the videos overall (p = 0.03). Automated coding offers a reliable, quantitative method for detecting atypical social orienting and reduced sustained attention in toddlers with autism spectrum disorder. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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