Multimodal Machine Learning Approaches to Detect Attention-Deficit/Hyperactivity Disorder (ADHD) Using Physiological...

Background: Attention Deficit Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder characterised by persistent symptoms of inattention, impulsivity, and hyperactivity that can affect individuals throughout childhood and adulthood. Accurate and timely detection is important for appropriate support and management, but current assessment relies heavily on behavioural observations, clinical assessments, and information from multiple sources, which can be time-consuming and may be influenced by subjective judgement. These challenges have increased interest in machine learning (ML) and deep learning (DL) approaches that can analyse multiple physiological and behavioural data sources. Multimodal AI approaches may provide a more comprehensive and objective way to detect ADHD by combining complementary information from different data types. Objectives: This systematic review aimed to examine the use of ML and DL methods for ADHD detection using multimodal physiological and behavioural biomarkers, with particular attention to data sources, modelling approaches, multimodal fusion strategies, and reported diagnostic performance. Methods: A systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines to identify studies published from 2015 to 2026 that applied machine learning (ML) or deep learning (DL) methods to multimodal data for ADHD detection. We selected studies using predefined inclusion and exclusion criteria and extracted and synthesised data from eligible studies. The review included 25 studies. The multimodal data sources examined included neuroimaging modalities, such as functional magnetic resonance imaging (fMRI) and magnetic resonance imaging (MRI); physiological signals, including electroencephalography (EEG), heart rate variability (HRV), and electrodermal activity (EDA); behavioural assessments; and wearable or Internet of Things (IoT)-based measures. We also assessed the included studies for methodological quality and model performance to identify current evidence, limitations, and future research needs. Results: The included studies varied substantially in design and sample size, with participant numbers ranging from 22 to 15,883. Most studies used cross-sectional designs. Early multimodal fusion approaches frequently reported diagnostic accuracies above 80%, demonstrating the potential value of combining complementary physiological and behavioural information. Reported informative biomarkers included frontal brain activity, heart rate patterns, and oculomotor behaviour. However, substantial differences in data sources, acquisition procedures, feature extraction, modelling techniques, and evaluation protocols limited direct comparison across studies. Small sample sizes and a lack of standardised data collection and methodological protocols also challenged the generalisability and reliability of the reported findings. Conclusions: Multimodal machine learning and deep learning approaches show considerable potential for improving ADHD detection by integrating complementary physiological and behavioural information. However, substantial heterogeneity in data collection, devices, datasets, validation procedures, and reporting limits comparability and generalisability across studies. Future research should prioritise standardised data-collection protocols, larger and more diverse datasets (including children, adolescents, and participants from different cultural backgrounds), leakage-safe validation, and independent multi-site testing. Fusion strategies should be selected based on modality compatibility and study objectives. Improving model interpretability alongside predictive performance will also support clinical trust and real-world adoption.