Email this article DIGITAL IMAGE PROCESSING FOR NEURODEVELOPMENTAL DISORDERS: A RESEARCH GAP ANALYSIS AND FUTURE DIRECTIONS
Abstract
Neurodevelopmental disorders (NDDs), such as Autism Spectrum Disorder (ASD), Attention Deficit Hyperactivity Disorder (ADHD), and intellectual disabilities, affect millions of individuals worldwide, often manifesting in early childhood and persisting throughout life. Despite growing interest in early and accurate diagnosis, existing diagnostic approaches remain subjective, resource-intensive, and inconsistent across clinical settings. Digital Image Processing (DIP), powered by artificial intelligence (AI), has emerged as a promising tool for objectively analyzing neuroimaging and behavioral data to support early detection and monitoring. This paper presents a comprehensive analysis of current progress in AI-driven DIP for NDDs, identifies significant research gaps—including limitations in datasets, interpretability, and generalizability—and proposes future directions for scalable, ethical, and clinically integrable solutions.
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