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نمایش مقاله

Title:

Application of artificial intelligence in attention-deficit hyperactivity disorder detection and response to treatment: A systematic review

Subject:

Artificial Intelligence and Brain Mapping

Presenter Author:
Name:	Reza HoseiniNaserabad

Author:
Name 1:	Reza HoseiniNaserabad - Presenter
Affiliation 1:	Department of Biomedical Engineering and Medical Physics, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Name 2:	Ahmad Shalbaf - Corresponding
Affiliation 2:	Department of Biomedical Engineering and Medical Physics, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Name 3:	Afshin Shoeibi
Affiliation 3:	Data Science and Computational Intelligence Institute, University of Granada, Spain

Name 4:	Ram Bilas Pachori
Affiliation 4:	Department of Electrical Engineering, Indian Institute of Technology Indore, Indore 453552, India

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Abstract

Original Research

introduction :

Attention-deficit hyperactivity disorder (ADHD) is a widespread neurodevelopmental condition that significantly impacts many children. This review systematically explores the application of artificial intelligence (AI), particularly conventional machine learning (ML) and deep learning (DL), in diagnosing ADHD and predicting treatment responses from clinical data (demographics, questionnaires, cognitive tests, and biological variables) and neuroimaging modalities including electroencephalogram (EEG), , functional magnetic resonance imaging (fMRI), structural MRI (sMRI), magnetoencephalogram (MEG), diffusion tensor imaging (DTI), single photon emission computed tomography (SPECT), functional near-infrared spectroscopy (fNIRS), and positron emission tomography (PET).

Methods:

We searched papers published until October 2024 via Scopus, Web of Science (WOS), and PubMed and reviewed 147 studies on ADHD diagnosis and treatment response prediction. This study's primary contribution is the broad integration of its studies on both diagnosis and response prediction of pharmacological and non-pharmacological treatments in ADHD using AI methods, relying on feature extraction/selection methods and biomarkers rather than focusing on just one part. Furthermore, it is the first review to specifically evaluate the application of AI in predicting response to ADHD treatment. To have a better view, we investigated the data in clinical/demographic categories, neuroimaging techniques, and using neuroimaging/clinical biomarkers for ADHD diagnosis and predicting response to treatment.

Results:

Our findings emphasize the widespread application of AI in ADHD detection and show promising results with EEG signals (accuracy up to 99.95%) and MRI modalities (accuracy up to 92.8% with a combination of sMRI and fMRI) data while highlighting the limited application of AI in predicting treatment responses. Support vector machines (SVMs) and convolutional neural networks (CNNs) methods have been used more among AI methods. Also, extracting and selecting features from EEG signals is more prevalent than other neuroimaging techniques, and functional connectivity biomarkers in fMRI showed superior performance. Future research should aim to develop integrated AI models that can accurately diagnose and predict personalized treatment responses.

Conclusion:

AI holds tremendous promise in assisting the detection and treatment of ADHD. Conventional ML and advanced neural methods, particularly those dealing with neuroimaging modalities, have demonstrated relatively high levels of discriminative ability in differentiating people with ADHD from HC's. In contrast, the application of AI to predict the response to treatments remains significantly underdeveloped. Some studies predicted responses to stimulant medications such as methylphenidate through ML, while, to date, the field is lacking in robust research that offers predictions concerning reactions to a broad range of treatments, including non-pharmacological ones. Also, the potential of DL is still largely unexplored in this context. Future research should emphasize integrating AI models for collective ADHD diagnosis and treatment response prediction across treatment modalities. This would enable more correct and individualized interventions that improve patient outcomes and reduce healthcare costs. Realizing the full potential of DL in this domain will require larger, more diverse datasets. The incorporation of explainable AI techniques would add transparency to these models and encourage acceptance within the clinic.

Keywords:

ADHD, Neuroimaging, Artificial intelligence, Response prediction to treatment.