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

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.