A Novel Approach to ADHD Detection Using Handwriting and Image Processing

Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that begins in childhood and persists throughout life. Currently, there are no auxiliary tests, studies, or biomarkers with sufficient predictive power to confirm or exclude ADHD. Existing diagnostic methods, such as functional magnetic resonance imaging (fMRI), are both cumbersome and highly challenging for children. However, simpler methods could assist in diagnosing ADHD more effectively. In this study, significant results were achieved by utilizing handwriting for ADHD diagnosis through a very brief and straightforward process. A sentence written by a child on paper was successfully classified using image processing techniques and an intelligent model. The developed model serves as an assistive tool for medical experts by enabling preliminary diagnosis and early detection of ADHD. This study aims to both accelerate the diagnostic process and facilitate early diagnosis of ADHD. The obtained results demonstrate that preliminary diagnosis can be performed in a simple and rapid manner using handwriting analysis and image processing techniques.