Behavioral Phenotyping for Autism Spectrum Disorder Biomarkers Using Computer Vision
Behavioral Phenotyping for Autism Spectrum Disorder Biomarkers Using Computer Vision
复制标题
使用计算机视觉对自闭症谱系障碍生物标志物进行行为表型分析
DOI：
10.18178/joig.8.2.47-52
复制
发表时间：
2020
影响因子：
--
通讯作者：
P. Naval
中科院分区：
文献类型：
--
作者：
James;P. Naval
对儿童行为的分析对于早期发现自闭症谱系障碍(ASD)等发育障碍非常重要，自闭症谱系障碍通常以社交和沟通技能障碍以及重复和刻板行为为特征，几项关于这些行为的研究显示，在后来诊断为自闭症谱系障碍的儿童的录像中，这些行为表明有自闭症。为孤独症谱系障碍儿童的行为表型研究开发一种标准化、客观化的计算方法，对于数据收集、建立预测模型、筛选和监测这些模式具有重要意义。我们使用计算机视觉算法和方法创建了一个可扩展的基线应用程序，以可靠地捕获四个简单的生物标记物数据，用于使用头部姿势估计进行视觉注意跟踪，测量眨眼频率和身体姿势的特定可观察行为模式，以及检查ASD儿童张嘴外观的形态异常。我们的结论是，定量测量这些行为表型是可行的，并且通过测量简单的生物标记显示出至少三个生物标记的区分结果是有希望的结果。
Analysis of the behavior of children is important for the early detection of developmental disorders such as Autism Spectrum Disorder (ASD) that is usually characterized by impairments in social and communication skills and repetitive and stereotyped behaviors as several studies about these behaviors revealed indicative of ASD in recorded videos of children later diagnosed with ASD. Developing a computational approach that is standardized and objective for the behavioral phenotyping of children with autism spectral disorder is significant for data gathering for creating prediction model for screening and monitoring of these patterns. We create a scalable, baseline application using computer vision algorithms and methodologies to capture four simple biomarkers data reliably for visual attention tracking using head pose estimation, specific observable behavioral patterns measuring blink rate and body posture, and morphological anomalies examining open mouth appearance from children with ASD. We conclude that it is feasible to quantitatively measure these behavioral phenotypes and there are promising results from measuring simple biomarkers showing distinguishing results for at least three biomarkers.
遗传综合征中的行为表型：人类行为的遗传线索。
DOI：
--
发表时间：
2002
期刊：
Advances in pediatrics.
影响因子：
--
作者：
Cassidy,SuzanneB;Morris,ColleenA
通讯作者：
Morris,ColleenA