中国当代儿科杂志2024，Vol.26Issue(2)：151-157,7.DOI:10.7499/j.issn.1008-8830.2309073
不同意图场景眼动注视模式机器学习算法识别孤独症谱系障碍的研究
Machine learning algorithms for identifying autism spectrum disorder through eye-tracking in different intention videos
摘要
Abstract
Objective To investigate the differences in visual perception between children with autism spectrum disorder(ASD)and typically developing(TD)children when watching different intention videos,and to explore the feasibility of machine learning algorithms in objectively distinguishing between ASD children and TD children.Methods A total of 58 children with ASD and 50 TD children were enrolled and were asked to watch the videos containing joint intention and non-joint intention,and the gaze duration and frequency in different areas of interest were used as original indicators to construct classifier-based models.The models were evaluated in terms of the indicators such as accuracy,sensitivity,and specificity.Results When using eight common classifiers,including support vector machine,linear discriminant analysis,decision tree,random forest,and K-nearest neighbors(with K values of 1,3,5,and 7),based on the original feature indicators,the highest classification accuracy achieved was 81.90% .A feature reconstruction approach with a decision tree classifier was used to further improve the accuracy of classification,and then the model showed the accuracy of 91.43% ,the specificity of 89.80% ,and the sensitivity of 92.86% ,with an area under the receiver operating characteristic curve of 0.909(P<0.001).Conclusions The machine learning model based on eye-tracking data can accurately distinguish ASD children from TD children,which provides a scientific basis for developing rapid and objective ASD screening tools.[Chinese Journal of Contemporary Pediatrics,2024,26(2):151-157]关键词
孤独症谱系障碍/机器学习/联合意图/动作理解/眼动追踪/儿童Key words
Autism spectrum disorder/Machine learning/Joint intention/Action understanding/Eye tracking/Child引用本文复制引用
程蓉,赵众,侯文文,周刚,廖昊天,张雪,李晶..不同意图场景眼动注视模式机器学习算法识别孤独症谱系障碍的研究[J].中国当代儿科杂志,2024,26(2):151-157,7.基金项目
国家自然科学基金面上项目(31971009 （）
82171539) （）
中国科学院青年创新促进会项目(2021082). （2021082）
