ADHD Identification using Convolutional Neural Network with Seed-based Approach for fMRI Data

Ariyarathne, Gangani; Silva, Senuri De; Dayarathna, Sanuwani; Meedeniya, Dulani; Jayarathne, Sampath

doi:10.1145/3384544.3384552

Cited by 27 publications

(13 citation statements)

References 14 publications

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“…In computational fields, fundable and publishable research implementations often must include some innovation in software or hardware. Therefore, it was not surprising that many papers in our past reviews ( 6 ) related to proposing algorithms to assess ADHD using different machine learning approaches to classify brain activity ( 27 – 40 ). Similarly, papers often contributed to the scholarly discourse of developing novel prototypes ( 41 – 44 ) in which the efficacy had not been demonstrated.…”

Section: Methodsmentioning

confidence: 99%

Interdisciplinary Tensions When Developing Digital Interventions Supporting Individuals With ADHD

Cibrian

Monteiro

Schuck

et al. 2022

Front. Digit. Health

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Section: Methodsmentioning

confidence: 99%

Interdisciplinary Tensions When Developing Digital Interventions Supporting Individuals With ADHD

Cibrian

Monteiro

Schuck

et al. 2022

Front. Digit. Health

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“…Despite their promising results, they acknowledged that much work is still needed to localize the most discriminative sequences. Interestingly, a CNN using activation correlations from individual brain regions of the Default Mode Network (DMN) of the brain outperformed those using whole brain features (Ariyarathne et al, 2020). Using only one relevant brain region substantially reduced feature space and complexity.…”

Section: Classifiersmentioning

confidence: 99%

Machine Learning and MRI-based Diagnostic Models for ADHD: Are We There Yet?

et al. 2023

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Objective: Machine learning (ML) has been applied to develop magnetic resonance imaging (MRI)-based diagnostic classifiers for attention-deficit/hyperactivity disorder (ADHD). This systematic review examines this literature to clarify its clinical significance and to assess the implications of the various analytic methods applied. Methods: A comprehensive literature search on MRI-based diagnostic classifiers for ADHD was performed and data regarding the utilized models and samples were gathered. Results: We found that, although most studies reported the classification accuracies, they varied in choice of MRI modalities, ML models, cross-validation and testing methods, and sample sizes. We found that the accuracies of cross-validation methods inflated the performance estimation compared with those of a held-out test, compromising the model generalizability. Test accuracies have increased with publication year but were not associated with training sample sizes. Improved test accuracy over time was likely due to the use of better ML methods along with strategies to deal with data imbalances. Conclusion: Ultimately, large multi-modal imaging datasets, and potentially the combination with other types of data, like cognitive data and/or genetics, will be essential to achieve the goal of developing clinically useful imaging classification tools for ADHD in the future.

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“…Similarly, using the ABIDE dataset, which was based on rs-fMRI and deep neural network, ASD was distinguished from typically developing subjects [71]. Wireless Communications and Mobile Computing In addition, it was found that CNN algorithm was most efficient among all applied ML algorithms, and several studies have reported the rise in accuracy for ADHD diagnosis and examination by utilizing CNN with an accuracy range of between 90 ± 10 percent [55,[72][73][74][75][76][77][78]. Similarly, numerous studies were also conducted using CNN for ASD diagnosis and analyses showing a high accuracy rate > 70-90% [44,[79][80][81][82].…”

Section: Recent Machine Learning and Deep Learning Softwarementioning

confidence: 99%

Investigation of Machine Learning Methods for Early Prediction of Neurodevelopmental Disorders in Children

Alam

Raja

Gulzar

2022

Wireless Communications and Mobile Computing

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Several variables, for instance, inheritance and surroundings, influence the growth of neurodevelopmental disorders, e.g., autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) during the first 36 months of life. ADHD and ASD diagnosis mainly rely heavily on traditional clinical assessments from the last few decades. These traditional methods are based on massive data collection from multiple respondents’ responses and the extent of various behavioral descriptors, which are then recognized by the researcher while forming a diagnostic criterion. However, opting for traditional diagnostic methods, there is a high possibility of being misdiagnosed, which may lead to the administration of unnecessary long-term pharmaceutical treatment. That may lead to reduction in functioning and an increase in the risk of developing additional social and clinical issues. Moreover, such diagnostic procedures are also time-consuming and costly. In this sense, rapid and advanced criteria are required to be accurate and cost-effective. Consequently, this study emphasizes the application of machine learning (ML) tools and deep learning (DL) techniques such as convolutional neural network (CNN) and Deep Learning APIs (Application Programming Interface), for the early diagnosis and treatment of ADHD and ASD symptoms. From this investigation, it can be concluded that diagnostic techniques based on ML reduce the intervention time and increase the accuracy with simultaneous understanding of the techniques and algorithms applied to different types of Image data. Numerous studies have been done on ASD and ADHD separately, but our investigation also focuses on cooccurrences of these disorders in one individual.

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ADHD Identification using Convolutional Neural Network with Seed-based Approach for fMRI Data

Cited by 27 publications

References 14 publications

Interdisciplinary Tensions When Developing Digital Interventions Supporting Individuals With ADHD

Interdisciplinary Tensions When Developing Digital Interventions Supporting Individuals With ADHD

Machine Learning and MRI-based Diagnostic Models for ADHD: Are We There Yet?

Investigation of Machine Learning Methods for Early Prediction of Neurodevelopmental Disorders in Children

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