Abstract #

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by symptoms affecting social interaction, communication, and behavior, with diagnosis complicated by significant individual variability and the absence of definitive biomarkers. Current artificial intelligence methods have improved diagnostic accuracy, but their reliance on subjective assessments or single-modal data, coupled with their ``black-box" nature, limits consistency and clinical applicability. Addressing current limitations, this paper introduces a multimodal ASD detection framework using deep neural networks (DNN) with explainable AI (xAI) to enhance model transparency. Our model achieves a mean 5-fold cross-validation accuracy of 98.64% (± 0.86%), surpassing existing methods and demonstrating potential for clinical dependability of ASD diagnoses. The source code is available at: https://github.com/mebenyahia/Multimodal-Explainable-Automated-Diagnosis-of-Autistic-Spectrum-Disorder

Citation #