AI Article Synopsis
- The study examines the role of artificial intelligence in early screening and diagnosis of autism spectrum disorders (ASD), highlighting the need for improved detection methods due to subtle developmental differences.
- A review of 43 research articles categorized recognition markers into gaze behaviors, facial expressions, motor movements, voice features, and task performance, reporting AI screening accuracy ranging from 62.13% to 100%.
- The findings suggest that while AI recognition shows promise for identifying ASD in children, ongoing enhancements in the screening models are necessary for better accuracy and effective early intervention.
Article Abstract
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.
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| http://dx.doi.org/10.1007/s10803-024-06429-9 | DOI Listing |
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