Integrated Web-Based Platform for AI-Enhanced Autism Assessment

This research presents a novel web-based platform that leverages facial and vocal tracking to enhance the preliminary screening process for Autism Spectrum Disorder (ASD). By incorporating advanced machine learning algorithms to analyze multimodal data, the platform offers a non-invasive, accessible, and cost-effective solution for initial ASD evaluation. The system demonstrates high diagnostic accuracy, achieving 90% in facial pattern analysis, 85% in vocal pattern analysis, and 95% accuracy when both analyses are combined. Additionally, it includes communication features designed to foster a supportive environment for individuals with ASD. This paper provides an overview of the platform’s development, functionality, and future potential, with an emphasis on its capacity to improve existing diagnostic methods and its accessibility for underserved communities.