Application of artificial intelligence in modern healthcare for diagnosis of autism spectrum disorder.

Introduction: Symptoms of autism spectrum disorder (ASD) range from mild to severe and are evident in early childhood. Children with ASD have difficulties with social interaction, language development, and behavioral regulation. ASD is a mental condition characterized by challenges in communication, restricted behaviors, difficulties with speech, non-verbal interaction, and distinctive facial features in children. The early diagnosis of ASD depends on identifying anomalies in facial function, which may be minimal or missing in the first stages of the disorder. Due to the unique behavioral patterns shown by children with ASD, facial expression analysis has become an effective method for the early identification of ASD. Methods: Hence, utilizing deep learning (DL) methodologies presents an excellent opportunity for improving diagnostic precision and efficacy. This study examines the effectiveness of DL algorithms in differentiating persons with ASD from those without, using a comprehensive dataset that includes images of children and ASD-related diagnostic categories. In this research, ResNet50, Inception-V3, and VGG-19 models were used to identify autism based on the facial traits of children. The assessment of these models used a dataset obtained from Kaggle, consisting of 2,940 face images. Results: The suggested Inception-V3 model surpassed current transfer learning algorithms, achieving a 98% accuracy rate. Discussion: Regarding performance assessment, the suggested technique demonstrated advantages over the latest models. Our methodology enables healthcare physicians to verify the first screening for ASDs in children.