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 …

Machine learning in attention-deficit/hyperactivity disorder: new approaches toward understanding the neural mechanisms

M Cao, E Martin, X Li - Translational Psychiatry, 2023 - nature.com
Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent and heterogeneous
neurodevelopmental disorder in children and has a high chance of persisting in adulthood …

Individualized prediction models in ADHD: a systematic review and meta-regression

G Salazar de Pablo, R Iniesta, A Bellato, A Caye… - Molecular …, 2024 - nature.com
There have been increasing efforts to develop prediction models supporting personalised
detection, prediction, or treatment of ADHD. We overviewed the current status of prediction …

Predicting children with ADHD using behavioral activity: a machine learning analysis

M Maniruzzaman, J Shin, MAM Hasan - Applied Sciences, 2022 - mdpi.com
Attention deficit hyperactivity disorder (ADHD) is one of childhood's most frequent
neurobehavioral disorders. The purpose of this study is to:(i) extract the most prominent risk …

Neurological state changes indicative of ADHD in children learned via EEG-based LSTM networks

Y Chang, C Stevenson, IC Chen… - Journal of Neural …, 2022 - iopscience.iop.org
Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that
pervasively interferes with the lives of individuals starting in childhood. Objective. To …

It takes time: vigilance and sustained attention assessment in adults with ADHD

ABM Fuermaier, L Tucha, N Guo, C Mette… - International journal of …, 2022 - mdpi.com
Objectives: The present study compares the utility of eight different tests of vigilance and
sustained attention in the neuropsychological examination of adults with Attention …

Deep-learning-based ADHD classification using children's skeleton data acquired through the ADHD screening game

W Lee, D Lee, S Lee, K Jun, MS Kim - Sensors, 2022 - mdpi.com
The identification of attention deficit hyperactivity disorder (ADHD) in children, which is
increasing every year worldwide, is very important for early diagnosis and treatment …

Inclusion of a computerized test in ADHD diagnosis process can improve trust in the specialists' decision and elevate adherence levels

ES Grossman, I Berger - Scientific reports, 2024 - nature.com
Attention deficit and hyperactivity disorder (ADHD) affects many life aspects of children and
adults. Accurate identification, diagnosis and treatment of ADHD can facilitate better care …

Distinguishing different types of attention deficit hyperactivity disorder in children using artificial neural network with clinical intelligent test

IC Lin, SC Chang, YJ Huang, TBJ Kuo… - Frontiers in …, 2023 - frontiersin.org
Background Attention deficit hyperactivity disorder (ADHD) is a well-studied topic in child
and adolescent psychiatry. ADHD diagnosis relies on information from an assessment scale …

Neuronal correlates of task irrelevant distractions enhance the detection of attention deficit/hyperactivity disorder

CC Chen, EHK Wu, YQ Chen, HJ Tsai… - … on Neural Systems …, 2023 - ieeexplore.ieee.org
Early diagnosis and treatment can reduce the symptoms of Attention Deficit/Hyperactivity
Disorder (ADHD) in children, but medical diagnosis is usually delayed. Hence, it is important …