Automated detection of ADHD: Current trends and future perspective

Loh, Hui Wen; Ooi, Chui Ping; Barua, Prabal Datta; Palmer, Elizabeth E.; Molinari, Filippo; Acharya, URajendra

doi:10.1016/j.compbiomed.2022.105525

Cited by 68 publications

(41 citation statements)

References 151 publications

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“…As a result, an increasing rate of ADHD disorder misdiagnosis with other types of brain disorder has been reported [4]. Furthermore, it has also been reported that medical diagnosis of ADHD is often delayed due to the heterogeneous nature of ADHD, combined comorbidities and a global shortage of diagnostic clinicians [5]. Hence, alternative ways to increase the efficiency of early diagnosis is important [5].…”

Section: Introductionmentioning

confidence: 99%

Neuronal Correlates of Task Irrelevant Distractions Enhance the Detection of Attention Deficit/Hyperactivity Disorder

Chen

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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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 to increase the efficiency of early diagnosis. Previous studies used behavioral and neuronal data during GO/NOGO task to help detect ADHD and the accuracy differed considerably from 53% to 92%, depending on the employed methods and the number of electroencephalogram (EEG) channels. It remains unclear whether data from a few EEG channels can still lead to a good accuracy of detecting ADHD. Here, we hypothesize that introducing distractions into a VR-based GO/NOGO task can augment the detection of ADHD using 6-channel EEG because children with ADHD are easily distracted.Forty-nine ADHD children and 32 typically developing children were recruited. We use a clinically applicable system with EEG to record data. Statistical analysis and machine learning methods were employed to analyze the data. The behavioral results revealed significant differences in task performance when there are distractions. The presence of distractions leads to EEG changes in both groups, indicating immaturity in inhibitory control. Importantly, the distractions additionally enhanced the between-group differences in NOGO α and γ power, reflecting insufficient inhibition in different neural networks for distraction suppression in the ADHD group. Machine learning methods further confirmed that distractions enhance the detection of ADHD with an accuracy of 85.45%. In conclusion, this system can assist in fast screenings for ADHD and the findings of neuronal correlates of distractions can help design therapeutic strategies.

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Section: Introductionmentioning

confidence: 99%

Neuronal Correlates of Task Irrelevant Distractions Enhance the Detection of Attention Deficit/Hyperactivity Disorder

Chen

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…These networks consist of many neurons that need to be directly interconnected and synchronized ( Sato et al, 2012 ; Hernandez et al, 2015 ). It has been suggested that certain disorders in the human body arise when brain networks are incorrectly connected to manage a specific activity ( Gautam and Sharma, 2020 ; Noor et al, 2020 ; Khodatars et al, 2021 ; Loh et al, 2022 ). Disorders of this type can be classified into three groups based on their psychological or neural characteristics and threaten the health of many individuals across the globe.…”

Section: Introductionmentioning

confidence: 99%

Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review

Moridian

Ghassemi

Jafari

et al. 2022

Front. Mol. Neurosci.

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Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging.

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“…Various diagnostic tools are used for identifying ADHD: MRI, EEG, questionnaires, motion data, performance tests, etc. [12]. However, parents play a central role in recognizing such behavioral problems early in their children by their perception, awareness, and acceptance of the disease and prompt decision to accompany the child to a specialist [9,13].…”

Section: Introductionmentioning

confidence: 99%

Computerized Assessment of Cognitive Development in Neurotypical and Neurodivergent Children

Danilov¹,

Svajyan²,

Mihailova³

2022

OBM Neurobiol

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This study aims to observe the differences in the shared intentionality magnitude in mother-child dyads with neurotypical (NT) children and neurodivergent (ND) children aged 3-6 years. The quality of shared intentionality in infancy is associated with cognitive development. Our results showed that ND children scored six times higher (on average) in quiz-test than NT children. Children with difficulties in interaction (ND children) are more likely to use shared intentionality in conversation than NT children. We believe that this knowledge can contribute to developing computerized assessment methods which can diagnose developmental disabilities in such children.

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Automated detection of ADHD: Current trends and future perspective

Cited by 68 publications

References 151 publications

Neuronal Correlates of Task Irrelevant Distractions Enhance the Detection of Attention Deficit/Hyperactivity Disorder

Neuronal Correlates of Task Irrelevant Distractions Enhance the Detection of Attention Deficit/Hyperactivity Disorder

Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review

Computerized Assessment of Cognitive Development in Neurotypical and Neurodivergent Children

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