Jia Si-Jia, Jing Jia-Qi, Yang Chang-Jiang
Faculty of Education, East China Normal University, Shanghai, China.
China Research Institute of Care and Education of Infants and Young, Shanghai, China.
J Autism Dev Disord. 2024 Jun 6. doi: 10.1007/s10803-024-06429-9.
With the increasing prevalence of autism spectrum disorders (ASD), the importance of early screening and diagnosis has been subject to considerable discussion. Given the subtle differences between ASD children and typically developing children during the early stages of development, it is imperative to investigate the utilization of automatic recognition methods powered by artificial intelligence. We aim to summarize the research work on this topic and sort out the markers that can be used for identification.
We searched the papers published in the Web of Science, PubMed, Scopus, Medline, SpringerLink, Wiley Online Library, and EBSCO databases from 1st January 2013 to 13th November 2023, and 43 articles were included.
These articles mainly divided recognition markers into five categories: gaze behaviors, facial expressions, motor movements, voice features, and task performance. Based on the above markers, the accuracy of artificial intelligence screening ranged from 62.13 to 100%, the sensitivity ranged from 69.67 to 100%, the specificity ranged from 54 to 100%.
Therefore, artificial intelligence recognition holds promise as a tool for identifying children with ASD. However, it still needs to continually enhance the screening model and improve accuracy through multimodal screening, thereby facilitating timely intervention and treatment.
随着自闭症谱系障碍（ASD）患病率的不断上升，早期筛查和诊断的重要性受到了广泛讨论。鉴于ASD儿童与正常发育儿童在发育早期存在细微差异，研究人工智能驱动的自动识别方法的应用势在必行。我们旨在总结该主题的研究工作，并梳理出可用于识别的标志物。
我们检索了2013年1月1日至2023年11月13日在Web of Science、PubMed、Scopus、Medline、SpringerLink、Wiley Online Library和EBSCO数据库中发表的论文，共纳入43篇文章。
这些文章主要将识别标志物分为五类：注视行为、面部表情、运动动作、语音特征和任务表现。基于上述标志物，人工智能筛查的准确率在62.13%至100%之间，灵敏度在69.67%至100%之间，特异性在54%至100%之间。
因此，人工智能识别有望成为识别ASD儿童的工具。然而，它仍需不断完善筛查模型，并通过多模式筛查提高准确性，从而促进及时干预和治疗。
