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Attention deficit/hyperactivity disorder (ADHD) is characterized by symptoms of inattention, hyperactivity, and impulsivity, which affects an estimated 10.2% of children and adolescents in the United States. However, correct diagnosis of the condition can be challenging, with failure rates up to 20%. Machine learning models making use of magnetic resonance imaging (MRI) have the potential to serve as a clinical decision support system to aid in the diagnosis of ADHD in youth to improve diagnostic validity. The purpose of this study was to develop and evaluate an explainable deep learning model for automatic ADHD classification. 254 T1-weighted brain MRI datsets of youth aged 9-11 were obtained from the Adolescent Brain Cognitive Development (ABCD) Study, and the Child Behaviour Checklist DSM-Oriented ADHD Scale was used to partition subjects into ADHD and non-ADHD groups. A fully convolutional neural network (CNN) adapted from a state-of-the-art adult brain age regression model was trained to distinguish between the neurologically normal children and children with ADHD. Saliency voxel attribution maps were generated to identify brain regions relevant for the classification task. The proposed model achieved an accuracy of 71.1%, sensitivity of 68.4%, and specificity of 73.7%. Saliency maps highlighted the orbitofrontal cortex, entorhinal cortex, and amygdala as important regions for the classification, which is consistent with previous literature linking these regions to significant structural differences in youth with ADHD. To the best of our knowledge, this is the first study applying artiicial intelligence explainability methods such as saliency maps to the classification of ADHD using a deep learning model. The proposed deep learning classification model has the potential to aid clinical diagnosis of ADHD while providing interpretable results.
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