Application of Artificial Intelligence in Supporting Healthcare Professionals and Caregivers in Treatment of Autistic Children

TL;DR

This paper explores how AI, particularly deep learning models like Xception and ResNet50V2, can enhance the diagnosis and management of Autism Spectrum Disorder by analyzing expressions during daily activities.

Contribution

It introduces a novel AI-based system that analyzes facial and bodily expressions to improve ASD diagnosis and management, demonstrating high accuracy with specific deep learning architectures.

Findings

AI models achieved high diagnostic accuracy

Deep learning effectively analyzes expressions during daily activities

Potential for AI to support personalized ASD treatment

Abstract

Autism Spectrum Disorder (ASD) represents a multifaceted neurodevelopmental condition marked by difficulties in social interaction, communication impediments, and repetitive behaviors. Despite progress in understanding ASD, its diagnosis and treatment continue to pose significant challenges due to the variability in symptomatology and the necessity for multidisciplinary care approaches. This paper investigates the potential of Artificial Intelligence (AI) to augment the capabilities of healthcare professionals and caregivers in managing ASD. We have developed a sophisticated algorithm designed to analyze facial and bodily expressions during daily activities of both autistic and non-autistic children, leading to the development of a powerful deep learning-based autism detection system. Our study demonstrated that AI models, specifically the Xception and ResNet50V2 architectures, achieved high accuracy in diagnosing Autism Spectrum Disorder (ASD). This research highlights the transformative potential of AI in improving the diagnosis, treatment, and comprehensive management of ASD. Our study revealed that AI models, notably the Xception and ResNet50V2 architectures, demonstrated high accuracy in diagnosing ASD.