Machine learning in automated diagnosis of autism spectrum disorder: a comprehensive review

IF 13.3 1区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Science Review Pub Date : 2025-02-01 DOI:10.1016/j.cosrev.2025.100730

Khosro Rezaee

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引用次数: 0

Abstract

Autism Spectrum Disorder (ASD) is a multifaceted neurodevelopmental condition characterized by social communication challenges, repetitive behaviors, and restricted interests. Early and accurate diagnosis is paramount for effective intervention and treatment, significantly improving the quality of life for individuals with ASD. This comprehensive review aims to elucidate the various methodologies employed in the automated diagnosis of ASD, providing a comparative analysis of their diagnostic accuracy, privacy considerations, non-invasiveness, cost implications, computational complexity, and feasibility for clinical and therapeutic use. The study encompasses a wide range of techniques including neuroimaging, EEG signal analysis, speech and crying signal analysis, eye tracking, facial recognition, and body movement analysis, highlighting their potential and limitations in the context of ASD diagnosis. By exploring these diverse diagnostic approaches, the review seeks to offer insights into the most promising methods and identify areas for future research and development.

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来源期刊

Computer Science Review Computer Science-General Computer Science

CiteScore

32.70

自引率

0.00%

发文量

审稿时长

51 days

期刊介绍： Computer Science Review, a publication dedicated to research surveys and expository overviews of open problems in computer science, targets a broad audience within the field seeking comprehensive insights into the latest developments. The journal welcomes articles from various fields as long as their content impacts the advancement of computer science. In particular, articles that review the application of well-known Computer Science methods to other areas are in scope only if these articles advance the fundamental understanding of those methods.