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A comprehensive review of AI-based autism spectrum disorder analysis and prediction

human-computer-interaction
artificial-intelligence
data-mining-and-machine-learning
data-science
neural-networks

Abstract

One of the developmental illnesses with the greatest rate of growth in the world today is autism spectrum disorder (ASD), typified by difficulties with social communication, repetitive activities, and narrow interests. Effective intervention depends on early discovery, yet conventional diagnostic techniques can be subjective and time-consuming. Artificial intelligence (AI) has become a potent diagnostic tool for ASD in recent years, utilizing data from structural and functional MRI (s/fMRI), eye gazing, skeletal movement, facial images, EEG, and diagnostic questionnaires. In this paper, we focus on research conducted from 2019 onwards, discussing the significance of NIfTI (Neuroimaging Informatics Technology Initiative) in detecting various brain diseases. Additionally, we introduce a publicly available dataset for autism and provide an overview of the symptoms related to autism, particularly in communication and behavior. Furthermore, we present the preprocessing steps involved in neuroimaging data analysis. Lastly, we offer recommendations and highlight future opportunities in autism research, emphasizing potential advancements in early diagnosis, and the integration of artificial intelligence for more accurate and efficient analysis of neuroimaging data in the future.
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