Meng Fanchao, Li Fenghua, Wu Shuxian, Yang Tingyu, Xiao Zhou, Zhang Yujian, Liu Zhengkui, Lu Jianping, Luo Xuerong
The National Clinical Research Center for Mental Disorder & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.
Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
Front Neurosci. 2023 Sep 15;17:1170951. doi: 10.3389/fnins.2023.1170951. eCollection 2023.
Studies on eye movements found that children with autism spectrum disorder (ASD) had abnormal gaze behavior to social stimuli. The current study aimed to investigate whether their eye movement patterns in relation to cartoon characters or real people could be useful in identifying ASD children.
Eye-tracking tests based on videos of cartoon characters and real people were performed for ASD and typically developing (TD) children aged between 12 and 60 months. A three-level hierarchical structure including participants, events, and areas of interest was used to arrange the data obtained from eye-tracking tests. Random forest was adopted as the feature selection tool and classifier, and the flattened vectors and diagnostic information were used as features and labels. A logistic regression was used to evaluate the impact of the most important features.
A total of 161 children (117 ASD and 44 TD) with a mean age of 39.70 ± 12.27 months were recruited. The overall accuracy, precision, and recall of the model were 0.73, 0.73, and 0.75, respectively. Attention to human-related elements was positively related to the diagnosis of ASD, while fixation time for cartoons was negatively related to the diagnosis.
Using eye-tracking techniques with machine learning algorithms might be promising for identifying ASD. The value of artificial faces, such as cartoon characters, in the field of ASD diagnosis and intervention is worth further exploring.
眼动研究发现，自闭症谱系障碍（ASD）儿童对社会刺激的注视行为异常。本研究旨在调查他们对卡通人物或真实人物的眼动模式是否有助于识别ASD儿童。
对12至60个月大的ASD儿童和发育正常（TD）儿童进行基于卡通人物和真实人物视频的眼动测试。采用包括参与者、事件和感兴趣区域的三级层次结构来整理从眼动测试中获得的数据。采用随机森林作为特征选择工具和分类器，将展平后的向量和诊断信息用作特征和标签。使用逻辑回归来评估最重要特征的影响。
共招募了161名儿童（117名ASD儿童和44名TD儿童），平均年龄为39.70±12.27个月。该模型的总体准确率、精确率和召回率分别为0.73、0.73和0.75。对与人类相关元素的关注与ASD诊断呈正相关，而对卡通片的注视时间与诊断呈负相关。
将眼动追踪技术与机器学习算法结合使用可能有助于识别ASD。人工面孔，如卡通人物，在ASD诊断和干预领域的价值值得进一步探索。
