AI Integrated Support System for Detecting Autism Spectrum Disorder in Children

Autism Spectrum Disorder (ASD) is a developmental disorder that is often linked to expensive medical care and drawn-out evaluations. These difficulties can be lessened with early identification of ASD characteristics. Conventional approaches to diagnosing ASD frequently entail laborious, subjective, and ineffective processes based on expert knowledge. The adoption of clever AI-based algorithms that can identify patterns from past ASD cases and healthy people is a more promising method of expediting the diagnostic procedure. Compared to current techniques, this strategy seeks to be more accurate and efficient. The suggested system consists of an intelligent web service that employs a Convolutional Neural Network (CNN) and a mobile application that gathers data via questionnaires. By continuously learning from the data, the CNN-based system gradually increases its accuracy. In comparison to other traditional methods, this approach performed better when tested, especially when it came to identifying cases that were not ASD. With improved sensitivity, specificity, and accuracy, it proved to be a useful tool for medical practitioners. By improving the effectiveness and efficiency of the diagnosis process, this approach has the potential to completely transform the way ASD is diagnosed in the future.