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Year : 2021  |  Volume : 69  |  Issue : 6  |  Page : 1518--1523

Machine Learning Techniques for the Diagnosis of Attention-Deficit/Hyperactivity Disorder from Magnetic Resonance Imaging: A Concise Review

1 Department of Instrumentation and Control, National Institute of Technology, Tiruchirappalli, Tamil Nadu, India
2 Department of Electronics and Instrumentation, St. Joseph's College of Engineering and Technology, Palai, Kerala, India
3 Department of Neurology, K. A. P Viswanatham Government Medical College and MGM Government Hospital, Tiruchirappalli, Tamil Nadu, India

Correspondence Address:
Dr. R Periyasamy
Department of Instrumentation and Control, National Institute of Technology, Tiruchirappalli – 620 015, Tamil Nadu
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/0028-3886.333520

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Background: Attention-deficit/hyperactivity disorder (ADHD) is a neuro-developmental disease commonly seen in children and it is diagnosed via extensive interview procedures, behavioral studies, third-party observations, and comprehensive personal history. ADHD causes regional atrophy in brain regions and alters the pattern of functional brain connectivity networks. Automated/computerized methods based on magnetic resonance imaging (MRI) can replace subjective methods for the identification of ADHD. Objectives: The aim of this study was to analyze various machine-learning algorithms for ADHD by feeding in vital input features extracted from functional brain connectivity and different existing methods and to review factors crucial for the diagnosis of ADHD. Methods: This paper is a concise review of machine learning methods for the diagnosis of ADHD from MRI. Techniques for feature extraction, dimensionality reduction/feature selection, and classification, employed in the computerized techniques for the diagnosis of ADHD from MRI and the accuracy of classification offered by the individual methods, are focussed on the review. Conclusions: Machine learning algorithms with features of functional brain connectivity networks as input, with hierarchical sparse feature elimination, exhibits the highest accuracy. Augmentation of the behavioral features does not contribute much to increased accuracy. The level of accuracy offered by the frameworks meant for the computer-aided diagnosis of ADHD, available in the literature, does not justify their feasibility in clinical practice. Computerized methods that exploit highly specific biomarkers of ADHD like brain iron concentration in Globus Pallidus, Putamen, Caudate nucleus, and thalamus as features are not available.


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