A Review on Autism Spectrum Disorder

Abstract

Autism Spectrum Disorder is a neurodevelopmental disorder that involves difficulties with social communication and interaction, which can often be detected within the first two years of life. It is usually diagnosed in childhood but continues to influence people into adolescence and adulthood. With ASD affecting 1 in 44 children worldwide, the need for faster, accurate, and cost-effective diagnosis has become critical. The current methods are time consuming, expensive, and often subjective, leading to delays in treatment and impacting negatively on individuals and their families. This project utilizes Machine Learning techniques to improve the ASD diagnosis process through image and video analysis. Advanced ML models, by observing behavioral patterns, facial expressions, and gestures, can detect and classify autism levels. The approach is non-intrusive, scalable, and efficient, providing a faster, more accurate, and widely accessible solution. This will enhance the diagnostic process and outcomes for individuals with ASD and their families.