AIMC Topic: Attention Deficit Disorder with Hyperactivity

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Toward a fair, gender-debiased classifier for the diagnosis of attention deficit/hyperactivity disorder- a Machine-Learning based classification study.

BMC medical informatics and decision making
BACKGROUND: Attention deficit/hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder. Gender disparities in the diagnosis of ADHD have been reported, suggesting that females tend to be diagnosed later in life than males are. The...

A Decision Support System Based on multi-head convolutional and Recurrent Neural Networks for assisting physicians in diagnosing ADHD.

Computers in biology and medicine
BACKGROUND: Attention-Deficit Hyperactivity Disorder (ADHD) is highly prevalent among children and adolescents. Traditional diagnostic methods are subjective and time-consuming, underscoring the need for more objective diagnostic tools. Electroenceph...

Response inhibition deficits in unmedicated youth with ADHD: An ERP/sLORETA study.

Journal of affective disorders
BACKGROUND: Attention-Deficit/Hyperactivity Disorder (ADHD) is characterized by impairments in executive functioning, particularly response inhibition (RI). This study combines time-domain analysis and source analyses of event-related potentials (ERP...

Deep learning diagnosis plus kinematic severity assessments of neurodivergent disorders.

Scientific reports
Early diagnostic assessments of neurodivergent disorders (NDD), remains a major clinical challenge. We address this problem by pursuing the hypothesis that there is important cognitive information about NDD conditions contained in the way individuals...

Unraveling ADHD Through Eye-Tracking Procedures: A Scoping Review.

Journal of attention disorders
OBJECTIVE: This scoping review aimed to examine the application of eye-tracking technology in children with Attention-Deficit/Hyperactivity Disorder (ADHD), focusing on the scientific fields involved, methodologies employed, research goals, and outco...

Redefining parameter-efficiency in ADHD diagnosis: A lightweight attention-driven kolmogorov-arnold network with reduced parameter complexity and a novel activation function.

Psychiatry research. Neuroimaging
As deep learning continues to advance in medical analysis, the increasing complexity of models, particularly Convolutional Neural Networks (CNNs), presents significant challenges related to interpretability, computational costs, and real-world applic...

Exploring voice as a digital phenotype in adults with ADHD.

Scientific reports
Current diagnostic procedures for attention deficit hyperactivity disorder (ADHD) are mainly subjective and prone to bias. While research on potential biomarkers, including EEG, brain imaging, and genetics is promising, it has yet to demonstrate clin...

Converging Representations of Attention-Deficit/Hyperactivity Disorder and Autism on Social Media: Linguistic and Topic Analysis of Trends in Reddit Data.

Journal of medical Internet research
BACKGROUND: Social media platforms have witnessed a substantial increase in mental health-related discussions, with particular attention focused on attention-deficit/hyperactivity disorder (ADHD) and autism. This heightened interest coincides with gr...

Topology-Guided Graph Masked Autoencoder Learning for Population-Based Neurodevelopmental Disorder Diagnosis.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Exploring the pathogenic mechanisms of brain disorders within population is an important research in the field of neuroscience. Existing methods either combine clinical information to assist analysis or use data augmentation for sample expansion, ign...

Artificial intelligence for children with attention deficit/hyperactivity disorder: a scoping review.

Experimental biology and medicine (Maywood, N.J.)
Attention deficit/hyperactivity disorder is a common neuropsychiatric disorder that affects around 5%-7% of children worldwide. Artificial intelligence provides advanced models and algorithms for better diagnosis, prediction and classification of att...