2021
DOI: 10.4103/0028-3886.333520
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Machine Learning Techniques for the Diagnosis of Attention-Deficit/Hyperactivity Disorder from Magnetic Resonance Imaging

Abstract: 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… Show more

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Cited by 6 publications
(3 citation statements)
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“…This systematic review shows that ML may be useful in improving the accuracy of ADHD diagnosis. Previous systematic reviews suggest the same and have shown very promising results in ML for accurate and early detection of ADHD (Pereira-Sanchez & Castellanos, 2021;Periyasamy et al, 2021;Zhang-James et al, 2023), but the present review is only focusing on psychometric scales and questionnaires.…”
Section: Discussionmentioning
confidence: 80%
See 1 more Smart Citation
“…This systematic review shows that ML may be useful in improving the accuracy of ADHD diagnosis. Previous systematic reviews suggest the same and have shown very promising results in ML for accurate and early detection of ADHD (Pereira-Sanchez & Castellanos, 2021;Periyasamy et al, 2021;Zhang-James et al, 2023), but the present review is only focusing on psychometric scales and questionnaires.…”
Section: Discussionmentioning
confidence: 80%
“…To present date, only a few review studies have been published on the identification of ADHD from ML techniques. Of these few studies, most of them focus on assessment tools as magnetic resonance imaging (MRI)‐based classifiers for the diagnosis of ADHD (Pereira‐Sanchez & Castellanos, 2021; Periyasamy et al, 2021; Zhang‐James et al, 2023). The present systematic review aims to study the scientific literature on ML studies on psychometric questionnaires for the diagnosis of ADHD.…”
Section: Introductionmentioning
confidence: 99%
“…These technologies could provide valuable insights into the child's attention, impulsivity, and hyperactivity levels, enabling convenient interventions and support. [7] Furthermore, there is a lack of research examining the ethical considerations and potential risks associated with AI and ML interventions for primary school ADHD children. The use of AI and ML algorithms extends concerns about privacy, data security, and the potential for unintended consequences.…”
Section: C) Research Gapmentioning
confidence: 99%