Using artificial intelligence methods to study the effectiveness of exercise in patients with ADHD

D Yu, J Fang - Frontiers in Neuroscience, 2024 - frontiersin.org
Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder
that significantly affects children and adults worldwide, characterized by persistent …

Evaluating the Neuroimaging-Genetic Prediction of Symptom Changes in Individuals with ADHD

P Suresh, B Ray, K Duan, J Chen… - 2021 43rd Annual …, 2021 - ieeexplore.ieee.org
Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that could
persist into adulthood with known abnormalities in brain structure. Genetics also play an …

The Hybrid Deep Learning Model for Identification of Attention-Deficit/Hyperactivity Disorder Using EEG

N Chugh, S Aggarwal, A Balyan - Clinical EEG and …, 2024 - journals.sagepub.com
Common misbehavior among children that prevents them from paying attention to tasks and
interacting with their surroundings appropriately is attention-deficit/hyperactivity disorder …

[HTML][HTML] Efficacy of novel summation-based synergetic artificial neural network in ADHD diagnosis

J Peng, M Debnath, AK Biswas - Machine Learning with Applications, 2021 - Elsevier
Abstract Attention Deficit Hyperactivity Disorder (ADHD) is a critical condition that affects
millions of children and often continues into adulthood. In this paper, we propose a dual 3D …

[HTML][HTML] Insight into ADHD diagnosis with deep learning on Actimetry: Quantitative interpretation of occlusion maps in age and gender subgroups

P Amado-Caballero, P Casaseca-de-la-Higuera… - Artificial Intelligence in …, 2023 - Elsevier
Abstract Attention Deficit/Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental
disorder in childhood that often persists into adulthood. Objectively diagnosing ADHD can …

ADHD diagnosis guided by functional brain networks combined with domain knowledge

C Cao, H Fu, G Li, M Wang, X Gao - Computers in Biology and Medicine, 2024 - Elsevier
Utilizing functional magnetic resonance imaging (fMRI) to model functional brain networks
(FBNs) is increasingly prominent in attention-deficit/hyperactivity disorder (ADHD) research …

Automated detection of ADHD: Current trends and future perspective

HW Loh, CP Ooi, PD Barua, EE Palmer… - Computers in Biology …, 2022 - Elsevier
Attention deficit hyperactivity disorder (ADHD) is a heterogenous disorder that has a
detrimental impact on the neurodevelopment of the brain. ADHD patients exhibit …

Recent Advances of Artificial Intelligence Tools in Attention-Deficit Hyperactivity Disorder (ADHD)

S Walvekar, B Thawkar… - Current …, 2022 - ingentaconnect.com
Attention deficit hyperactive disorder or ADHD is a common disorder among children, and if
not identified early, it may affect the child's later life. Pharmacotherapy in ADHD has been …

[HTML][HTML] ADHD/CD-NET: automated EEG-based characterization of ADHD and CD using explainable deep neural network technique

HW Loh, CP Ooi, SL Oh, PD Barua, YR Tan… - Cognitive …, 2023 - Springer
In this study, attention deficit hyperactivity disorder (ADHD), a childhood
neurodevelopmental disorder, is being studied alongside its comorbidity, conduct disorder …

[HTML][HTML] Hyperactivity and motoric activity in ADHD: characterization, assessment, and intervention

C Gawrilow, J Kühnhausen, J Schmid… - Frontiers in …, 2014 - frontiersin.org
The aim of the present literature review is threefold.(1) We will review theories, models, and
studies on symptomatic hyperactivity and motoric activity in attention-deficit/hyperactivity …