Application of Machine Learning to Physiological and Neuroanatomical Data in the Field of ADHD Diagnosis

Attention Deficit\Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder characterized by inattention, hyperactivity, and impulsivity. While traditional diagnostic methods rely on clinical interviews, tests and behavioral observations, machine learning methods provide an opportunity to simplify the ADHD diagnostic process and make it more accurate. This review tries to explore the application of machine learning (ML) algorithms to physiological and neuroanatomical data: magnetic resonance imaging (MRI), functional MRI (fMRI), near-infrared spectroscopy (fNIRS), electroencephalography (EEG), magnetoencephalography (MEG), electrocardiogram (ECG), pupil parameters, eye tracking and activity in the field of exploring biomarkers for ADHD diagnosis. Deep learning models and support vector machines (SVM) are considered the most promising approaches for identifying ADHD in both children and adults. However, despite the fact that with the help of machine learning methods researchers are able to achieve high levels of specificity and sensitivity when solving problems of ADHD assessment, their use in clinical practice requires preliminary work to verify the results on large samples, as well as addressing data security and ethical issues.

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