A Systematic Review of Tools for Attention Deficit/Hyperactivity Disorder Classification

Attention Deficit/Hyperactivity Disorder (ADHD) is one of the common mental disorders affecting children and is often diagnosed with an important delay. This neurodevelopmental disorder is usually identified by symptoms such as a deficit in attention, hyperactivity and impulsiveness. There are clinical and data-oriented methods for ADHD diagnosis. Recently, artificial intelligence techniques for ADHD diagnosis have been taken into consideration. In this article, we have reviewed state-of-the-art deep learning and classical machine learning networks using a wide range of neuroimaging, neurosignal and related data, including structural and functional magnetic resonance imaging (MRI), electroencephalography (EEG), magnetoencephalography (MEG), questionnaires, genome data, mo-tion data, performance tests and game simulation. In addition, we identified common validation methods and analyzed them. As an important aspect for clinicians, the brain regions of ADHD patients identified as discriminative by existing machine learning-based studies are investigated. In the end, we identify exiting research gaps, notably the lack of publicly available large-scale multimodal datasets, non-interpretability and inexplicability of many algorithms and the use of architectures that are unable to efficiently model the spatiotemporal features of the brain. Ultimately, we hope that researchers will implement explainable machine learning-based studies that use several neuroimaging modalities to aid clinicians in supporting their decisions for diagnosis, prognosis and for treatment planning.