A systematic literature review of machine learning techniques for the detection of attention-deficit/hyperactivity disorder using MRI and/or EEG data

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental condition common in teenagers across the globe. Neuroimaging and Machine Learning (ML) advancements have revolutionized its diagnosis and treatment approaches. Although, the researchers are continuously developing automated ADHD diagnostic tools, there is no reliable ML-based diagnostic system for clinicians. Thus, the study aims to systematically review ML and DL-based approaches for ADHD diagnosis, leveraging brain data from magnetic resonance imaging (MRI) and electroencephalogram (EEG) data. A methodical review for the period 2016 to 2022 is conducted by following the PRISMA guidelines. Four reputable repositories, namely PubMed, IEEE, ScienceDirect, and Springer are searched for the related literature on ADHD diagnosis using MRI/EEG data. 87 studies are selected after screening abstracts of the papers. We critically conducted an analysis of these studies by examining various aspects related to training ML/DL-models, including diverse datasets, hyperparameter tuning, overfitting, and interpretability. The quality and risk assessment is conducted using the QUADAS2 tool to determine the bias due to patient selection, index test, reference standard, and flow and timing. Our rigours analysis observed significant diversity in dataset acquisition and its size, feature extraction and selection techniques, validation strategies and classifier choices. Our findings emphasize the need for generalizability, transparency, interpretability, and reproducibility in future research. The challenges and potential solutions associated with integrating diagnostic models into clinical settings are also discussed. The identified research gaps will guide researchers in developing a reliable ADHD diagnostic system that addresses the associated challenges.