Revolutionising ADHD diagnosis: deep learning in 3D medical imaging

Attention-Deficit/Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder, necessitating accurate diagnostic methods. Our research introduces a deep learning approach using Convolutional Neural Network (CNN) integrated with Bidirectional Long Short-Term Memory (BiLSTM) networks to analyse resting-state functional Magnetic Resonance Imaging (fMRI). This novel method captures intricate spatiotemporal patterns in brain activity, offering insights into ADHD characteristics that surpass traditional diagnostic techniques. Employing the ADHD-200 Sample, our study presents a comparative analysis demonstrating the enhanced efficacy of deep learning in ADHD diagnosis. The integration of CNN with BiLSTM allows for comprehensive analysis of fMRI data, revealing complex neural dynamics associated with ADHD. This approach marks a significant advancement in neuroimaging-based clinical neuroscience, potentially transforming ADHD diagnosis by providing a more objective, accurate, and efficient diagnostic tool. Our findings highlight the potential of deep learning technologies in medical imaging and diagnosis, opening new avenues for research and application in clinical neuroscience. The study underscores the importance of integrating advanced computational methods with clinical expertise to improve diagnostic accuracy and patient care in ADHD and potentially other neurodevelopmental disorders.