Computer vision analysis captures atypical attention in toddlers with autism.
Computer vision analysis captures atypical attention in toddlers with autism.
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DOI:
10.1177/1362361318766247
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发表时间:
2019-04
期刊:
影响因子:
--
通讯作者:
Dawson G
中科院分区:
文献类型:
--
作者:
Campbell K;Carpenter KL;Hashemi J;Espinosa S;Marsan S;Borg JS;Chang Z;Qiu Q;Vermeer S;Adler E;Tepper M;Egger HL;Baker JP;Sapiro G;Dawson G
To demonstrate the capability of computer vision analysis (CVA) to detect atypical orienting and attention behaviors in toddlers with autism spectrum disorder (ASD). 104 toddlers 16-31 months old (Mean=22) participated in this study. Twenty-two of the toddlers had ASD and 82 had typical development or developmental delay. Toddlers watched video stimuli on a tablet while the built-in camera recorded their head movement. CVA measured participants' attention and orienting in response to name calls. Reliability of the CVA algorithm was tested against a human rater. Differences in behavior were analyzed between the ASD group and the comparison group. Reliability between CVA and human coding for orienting to name was excellent (ICC 0.84, 95%CI 0.67-0.91). Only 8% of toddlers with ASD 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 ASD (2.02 vs 1.06 sec, p=0.04). Sensitivity for ASD of atypical orienting was 96%, and specificity was 38%. Older toddlers with ASD 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 ASD.
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