A Review of Artificial Intelligence Approaches in the Diagnosis of Attention-Deficit Hyperactivity Disorder
Subject Areas : Information Technology in Engineering Design (ITED) JournalTahereh Ziaaldini 1 , Mahboobeh Houshmand 2 , Seyyed Abed Hosseini 3 *
1 - Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.
2 - Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.
3 - Department of Electrical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.
Keywords: Attention deficit/hyperactivity disorder, Artificial intelligence, Machine learning, Deep learning.,
Abstract :
Attention deficit/hyperactivity disorder (ADHD) is a common neurobehavioral disorder in children and adolescents that involves limited attention, hyperactivity, and impulsivity. Accurate and timely diagnosis of this disorder is very important to provide appropriate therapeutic interventions. Meanwhile, approaches based on artificial intelligence can play an important role in improving and speeding up the diagnosis process. This research examines the use of artificial intelligence technologies in the diagnosis of ADHD. For this purpose, the clinical features and challenges in diagnosing ADHD are first introduced, then various artificial intelligence approaches such as machine learning, natural language processing, and deep learning are examined in this field. Also, case studies and experimental results of using these technologies are reviewed. Finally, the existing challenges and limitations as well as the future prospects of research in this field are discussed. This research provides an overview of the latest scientific advances in the use of artificial intelligence for the diagnosis of ADHD and can be used as a resource for clinicians and researchers in this field.
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[3] J. Hernández-Capistran, L. N. Sánchez-Morales, G. Alor-Hernández, M. Bustos-López, and J. L. Sánchez-Cervantes, “Machine and Deep Learning Algorithms for ADHD Detection: A Review,” in Innovations in Machine and Deep Learning, vol. 134, G. Rivera, A. Rosete, B. Dorronsoro, and N. Rangel-Valdez, Eds., in Studies in Big Data, vol. 134. , Cham: Springer Nature Switzerland, 2023, pp. 163–191. doi: 10.1007/978-3-031-40688-1_8.
[4] S. Khanna and W. Das, “A novel application for the efficient and accessible diagnosis of ADHD using machine learning,” in 2020 IEEE/ITU International Conference on Artificial Intelligence for Good (AI4G), IEEE, 2020, pp. 51–54, doi: 10.1109/AI4G50087.2020.9311012.
[5] S. Oh et al., “Diagnosis of ADHD using virtual reality and artificial intelligence: an exploratory study of clinical applications,” Frontiers in Psychiatry, vol. 15, p. 1383547, 2024, doi: 10.3389/fpsyt.2024.1383547.
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[8] I. Tachmazidis, T. Chen, M. Adamou, and G. Antoniou, “A hybrid AI approach for supporting clinical diagnosis of attention deficit hyperactivity disorder (ADHD) in adults,” Health Inf Sci Syst, vol. 9, no. 1, p. 1, Dec. 2021, doi: 10.1007/s13755-020-00123-7.
[9] J. Peng, M. Debnath, and A. K. Biswas, “Efficacy of novel summation-based synergetic artificial neural network in ADHD diagnosis,” Machine Learning with Applications, vol. 6, p. 100120, 2021, doi: 10.1016/j.mlwa.2021.100120.
[10] T. Chen, G. Antoniou, M. Adamou, I. Tachmazidis, and P. Su, “Automatic Diagnosis of Attention Deficit Hyperactivity Disorder Using Machine Learning,” Applied Artificial Intelligence, vol. 35, no. 9, pp. 657–669, Jul. 2021, doi: 10.1080/08839514.2021.1933761.
[11] I. Navarro-Soria, J. R. Rico-Juan, R. Juárez-Ruiz De Mier, and R. Lavigne-Cervan, “Prediction of attention deficit hyperactivity disorder based on explainable artificial intelligence,” Applied Neuropsychology: Child, pp. 1–14, Apr. 2024, doi: 10.1080/21622965.2024.2336019.
[12] G. Güney, E. Kisacik, C. KALAYCIOĞLU, and G. Saygili, “Exploring the attention process differentiation of attention deficit hyperactivity disorder (ADHD) symptomatic adults using artificial intelligence onelectroencephalography (EEG) signals,” Turkish Journal of Electrical Engineering and Computer Sciences, vol. 29, no. 5, pp. 2312–2325, 2021, doi: 10.3906/elk-2011-3.
[13] X. Chen et al., “Utilizing artificial intelligence-based eye tracking technology for screening ADHD symptoms in children,” Frontiers in Psychiatry, vol. 14, p. 1260031, 2023, doi: 10.3389/fpsyt.2023.1260031.
[14] D. C. Lohani and B. Rana, “ADHD diagnosis using structural brain MRI and personal characteristic data with machine learning framework,” Psychiatry Research: Neuroimaging, vol. 334, p. 111689, 2023, doi: 10.1016/j.pscychresns.2023.111689.
[15] H. Christiansen et al., “Use of machine learning to classify adult ADHD and other conditions based on the Conners’ Adult ADHD Rating Scales,” Scientific reports, vol. 10, no. 1, p. 18871, 2020, doi: 10.1038/s41598-020-75868-y.
[16] G. Wang, W. Li, S. Huang, and Z. Chen, “A Prospective Study of an Early Prediction Model of Attention Deficit Hyperactivity Disorder Based on Artificial Intelligence,” J Atten Disord, vol. 28, no. 3, pp. 302–309, Feb. 2024, doi: 10.1177/10870547231211360.
[17] A. Parashar, N. Kalra, J. Singh, and R. K. Goyal, “Machine learning based framework for classification of children with ADHD and healthy controls,” Intell. Autom. Soft Comput, vol. 28, no. 3, pp. 669–682, 2021, doi: 10.32604/iasc.2021.017478.
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