Deep learning with image-based autism spectrum disorder analysis: A systematic review

Autism spectrum disorder (ASD) is a collection of neuro-developmental disorders associated with social, communicational, and behavioral difficulties. Early detection thereof is necessary to mitigate the adverse effects of this disorder by initiating special education in schools and rehabilitation centers. Two methods are available for diagnosing and rehabilitating ASD. The first is the manual method (i.e., an observation- or interview-based approach), in which the disorder is diagnosed through observation or by interviewing a parent or caregiver. This method is time-consuming, subjective, and mostly comprises the examination of behavioral symptoms. The other method involves automatic diagnosis using traditional machine learning (ML)- and modern deep learning (DL)-based approaches that rely on image analysis. Indeed, the amount of research literature concerned with the evaluation of the usefulness of DL-based methods that process images or video data to diagnose ASD to improve patients’ lives has increased significantly. This paper presents a systematic review of the DL-based approach involving the analysis of images or videos in autism research. The review covers studies that were published from 2017 to June 2023 and were indexed in PubMed, IEEE Xplore, ACM Digital Library, and Google Scholar. The results are reported according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. A total of 130 studies were included in the analysis. Eligible papers were categorized based on the different features extracted as input for the DL-based approach. Existing well-known public and private datasets that include images or videos for autism research are extensively reviewed and discussed in this systematic review. In addition, different rehabilitation strategies that have been shown to be highly beneficial for ASD individuals are included. Finally, various challenges presented by the automated detection, classification, and rehabilitation of ASD are discussed. The review concludes that the use of DL for the precise and affordable diagnosis of autism is increasing substantially. Our findings are expected to significantly benefit researchers, therapists, psychologists, and relevant stakeholders to advance ASD screening, monitoring, and diagnosis with the aid of a DL-based approach that entails image or video analysis.