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The Use of Artificial Intelligence in Screening and Diagnosis of Autism Spectrum Disorder: A Literature Review

소아청소년정신의학 2019년 30권 4호 p.145 ~ 152
 ( Song Da-Yea ) - Seoul National University Bundang Hospital Department of Psychiatry

 ( Kim So-Yoon ) - Seoul National University Bundang Hospital Department of Psychiatry
 ( Bong Gui-Young ) - Seoul National University Bundang Hospital Department of Psychiatry
 ( Kim Jong-Myeong ) - Seoul National University Bundang Hospital Department of Psychiatry
 ( Yoo Hee-Jeong ) - Seoul National University Bundang Hospital Department of Psychiatry

Abstract


Objectives: The detection of autism spectrum disorder (ASD) is based on behavioral observations. To build a more objective datadriven method for screening and diagnosing ASD, many studies have attempted to incorporate artificial intelligence (AI) technologies. Therefore, the purpose of this literature review is to summarize the studies that used AI in the assessment process and examine whether other behavioral data could potentially be used to distinguish ASD characteristics.

Methods: Based on our search and exclusion criteria, we reviewed 13 studies.

Results: To improve the accuracy of outcomes, AI algorithms have been used to identify items in assessment instruments that are most predictive of ASD. Creating a smaller subset and therefore reducing the lengthy evaluation process, studies have tested the efficiency of identifying individuals with ASD from those without. Other studies have examined the feasibility of using other behavioral observational features as potential supportive data.

Conclusion: While previous studies have shown high accuracy, sensitivity, and specificity in classifying ASD and non-ASD individuals, there remain many challenges regarding feasibility in the real-world that need to be resolved before AI methods can be fully integrated into the healthcare system as clinical decision support systems.

키워드

Autism spectrum disorder; Artificial intelligence; Diagnosis; Screening
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