Kong Xue-Jun, Wei Zhen, Sun Binbin, Tu Yiheng, Huang Yiting, Cheng Ming, Yu Siyi, Wilson Georgia, Park Joel, Feng Zhe, Vangel Mark, Kong Jian, Wan Guobin
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.
Department of Child Psychiatry and Rehabilitation, Shenzhen Maternity and Child Healthcare Hospital, Southern Medical University, Shenzhen, China.
Front Psychiatry. 2022 Jun 9;13:899521. doi: 10.3389/fpsyt.2022.899521. eCollection 2022.
Children with autism spectrum disorder (ASD) have been observed to be associated with fixation abnormality as measured eye tracking, but the dynamics behind fixation patterns across age remain unclear.
In this study, we investigated gaze patterns between toddlers and preschoolers with and without ASD while they viewed video clips and still images (i.e., mouth-moving face, biological motion, mouthing face vs. moving object, still face picture vs. objects, and moving toys).
We found that the fixation time percentage of children with ASD showed significant decrease compared with that of TD children in almost all areas of interest (AOI) except for moving toy (helicopter). We also observed a diagnostic group (ASD vs. TD) and chronological age (Toddlers vs. preschooler) interaction for the eye AOI during the mouth-moving video clip. Support vector machine analysis showed that the classifier could discriminate ASD from TD in toddlers with an accuracy of 80% and could discriminate ASD from TD in preschoolers with an accuracy of 71%.
Our results suggest that toddlers and preschoolers may be associated with both common and distinct fixation patterns. A combination of eye tracking and machine learning methods has the potential to shed light on the development of new early screening/diagnosis methods for ASD.
通过眼动追踪测量发现，自闭症谱系障碍（ASD）儿童存在注视异常，但不同年龄段注视模式背后的动态变化尚不清楚。
在本研究中，我们调查了患有和未患有ASD的幼儿和学龄前儿童在观看视频片段和静态图像（即嘴巴运动的面部、生物运动、张嘴面部与移动物体、静态面部图片与物体以及移动玩具）时的注视模式。
我们发现，除了移动玩具（直升机）外，ASD儿童在几乎所有感兴趣区域（AOI）的注视时间百分比与发育正常（TD）儿童相比均显著降低。在嘴巴运动的视频片段中，我们还观察到了诊断组（ASD与TD）和实际年龄（幼儿与学龄前儿童）在眼睛AOI方面的交互作用。支持向量机分析表明，该分类器能够以80%的准确率区分幼儿中的ASD和TD，以71%的准确率区分学龄前儿童中的ASD和TD。
我们的结果表明，幼儿和学龄前儿童可能存在共同的和独特的注视模式。眼动追踪和机器学习方法相结合有可能为开发新的ASD早期筛查/诊断方法提供线索。
