A Review on Autism Spectrum Disorder Screening by Artificial Intelligence Methods.
Purpose: 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 i...
| Publicado en: | Journal of Autism & Developmental Disorders Vol. 55; no. 9; pp. 3011 - 3028 |
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| Autores principales: | , , |
| Formato: | research systematic review tables/charts Journal Article |
| Publicado: |
Springer Nature
Sep2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187434601&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187434601 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01623257 AUT jtl: Journal of Autism & Developmental Disorders issn: 01623257 maglogo: N pubinfo: dt: Sep2025 vid: 55 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 187434601 177689874 187434601 187434601 10.1007/s10803-024-06429-9 187434601 ppf: 3011 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Review on Autism Spectrum Disorder Screening by Artificial Intelligence Methods. aug: au: Jia, Si-Jia Jing, Jia-Qi Yang, Chang-Jiang affil: https://ror.org/02n96ep67 Faculty of Education, East China Normal University, Shanghai, China sug: subj: Autism Spectrum Disorder Diagnosis Health Screening Artificial Intelligence Methods Early Diagnosis Autism Spectrum Disorder Therapy Human Systematic Review PubMed Medline Eye Movements Facial Expression Motor Activity Voice Evaluation Task Performance and Analysis Sensitivity and Specificity Funding Source ab: Purpose: 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. Methods: 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. Results: 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%. Conclusion: 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. pubtype: Academic Journal doctype: research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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