A Review on Autism Spectrum Disorder Screening by Artificial Intelligence Methods
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ERIC Institute of Education Sciences
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| Main Authors: | , , |
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| Format: | Recurso educativo Open Access |
| Language: | en |
| Published: |
2025
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| _version_ | 1867181859326132224 |
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| author | Si-Jia Jia Jia-Qi Jing Chang-Jiang Yang |
| author_facet | Si-Jia Jia Jia-Qi Jing Chang-Jiang Yang Si-Jia Jia Jia-Qi Jing Chang-Jiang Yang |
| collection | Education Resources Information Center |
| contents | A Review on Autism Spectrum Disorder Screening by Artificial Intelligence Methods Si-Jia Jia Jia-Qi Jing Chang-Jiang Yang Autism Spectrum Disorders Screening Tests Clinical Diagnosis Artificial Intelligence Disability Identification Symptoms (Individual Disorders) Eye Movements Nonverbal Communication Psychomotor Skills Verbal Communication Performance Accuracy Intervention 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. |
| format | Recurso educativo Open Access |
| id | eric_EJ1481159 |
| institution | ERIC Institute of Education Sciences |
| language | en |
| publishDate | 2025 |
| record_format | eric |
| spellingShingle | A Review on Autism Spectrum Disorder Screening by Artificial Intelligence Methods Si-Jia Jia Jia-Qi Jing Chang-Jiang Yang Autism Spectrum Disorders Screening Tests Clinical Diagnosis Artificial Intelligence Disability Identification Symptoms (Individual Disorders) Eye Movements Nonverbal Communication Psychomotor Skills Verbal Communication Performance Accuracy Intervention A Review on Autism Spectrum Disorder Screening by Artificial Intelligence Methods Si-Jia Jia Jia-Qi Jing Chang-Jiang Yang Autism Spectrum Disorders Screening Tests Clinical Diagnosis Artificial Intelligence Disability Identification Symptoms (Individual Disorders) Eye Movements Nonverbal Communication Psychomotor Skills Verbal Communication Performance Accuracy Intervention 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. |
| title | A Review on Autism Spectrum Disorder Screening by Artificial Intelligence Methods |
| topic | Autism Spectrum Disorders Screening Tests Clinical Diagnosis Artificial Intelligence Disability Identification Symptoms (Individual Disorders) Eye Movements Nonverbal Communication Psychomotor Skills Verbal Communication Performance Accuracy Intervention |
| url | https://eric.ed.gov/?id=EJ1481159 |