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Using Brain Activation (nir-HEG/Q-EEG) and Execution Measures (CPTs) in a ADHD Assessment Protocol
Published on: April 1, 2018
From machine learning to deep learning in attention deficit hyperactivity disorder diagnosis: A bibliometric analysis
Mahi Khemchandani1, Manjusha Pradeep Deshmukh2
1Department of Engineering and Technology, Bharati Vidyapeeth (Deemed to be University), Pune, India.
Background:
The diagnosis of attention deficit hyperactivity disorder (ADHD) has traditionally relied on subjective clinical interviews. Recent years have witnessed a paradigm shift toward objective, data-driven diagnostics powered by artificial intelligence (AI).
Objective:
This study provides a comprehensive bibliometric review of AI and machine learning (ML) applications in ADHD prediction to map the field's evolution, current trends, and future directions.
Methods:
A structured search of the Scopus database retrieved 722 publications from 2011 to 2024. Bibliometric indicators were analyzed using Python and VOSviewer to evaluate annual production, geographical distribution, and technological trends.
Results:
The field has entered an exponential growth phase, with publication output peaking in 2023. A geopolitical analysis reveals a "research duopoly" between the United States (130 papers) and China (129 papers). Technologically, there is a distinct transition from traditional ML-support vector machines (SVM) to deep learning (DL) architectures. Crucially, electroencephalography (EEG) has emerged as the preferred neuroimaging modality over functional magnetic resonance imaging (fMRI) in recent AI studies, driven by its cost-effectiveness and high temporal resolution.
Conclusion:
AI-based ADHD diagnosis has matured from an exploratory niche to an evolving domain of computational psychiatry. Future research must prioritize explainable AI (XAI) and multimodal data fusion to translate high algorithmic accuracy into clinical utility.
