Application of Artificial Intelligence in Supporting Healthcare Professionals and Caregivers in Treatment of Autistic Children
It addresses the problem of variable symptomatology and multidisciplinary care needs in ASD for healthcare professionals and caregivers, but appears incremental as it applies existing deep learning methods to this domain.
This paper tackles the challenge of diagnosing Autism Spectrum Disorder (ASD) by developing an AI system that analyzes facial and bodily expressions, achieving high accuracy with models like Xception and ResNet50V2.
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.