Identification of Attention Deficit Hyperactivity Disorder Subtypes in Children Using Magnetic Resonance Imaging and Deep Learning

Abstract

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder that has become increasingly prevalent among school-going children in recent years. Traditional ADHD diagnosis relies on symptom-based assessments, which can be unreliable due to the evolving nature of symptoms over time. Accurate identification of ADHD and its subtypes is essential to avoid misdiagnosis and prevent further challenges for affected children. To address this, the present study developed a deep learning (DL)-based classification model using brain magnetic resonance imaging (MRI) to distinguish between typically developing (TD) children and those with ADHD subtypes (ADHD-Inattentive and ADHD-Combined). newlineInitially, grey matter (GM) and white matter (WM) were segmented from T1-weighted MRI scans using a modified fuzzy c-means clustering approach integrated with the elbow method. The MRI scans of TD children and those with ADHD subtypes were sourced from the publicly available ADHD-200 dataset. Total GM volume (GMV), GM volume in the frontal lobe (GMV_F), total WM volume (WMV), and WM volume in the frontal lobe (WMV_F) were computed using a novel method. These volumes were analyzed based on phenotypic information, including age, gender, medication status, and diagnosis. We found that the average GMV in children with ADHD-C is 2.34% higher compared to those with ADHD-I. Additionally, children with ADHD who are on medication exhibit a higher GMV compared to those not on medication, with a 2% increase for ADHD-C and a 2.5% increase for ADHD-I newline