Login Register Cart 0
Generic placeholder image

Current Psychopharmacology

Editor-in-Chief

ISSN (Print): 2211-5560
ISSN (Online): 2211-5579

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Review Article

Recent Advances of Artificial Intelligence Tools in Attention-Deficit Hyperactivity Disorder (ADHD)

Author(s): Shreya Walvekar, Baban Thawkar, Meena Chintamaneni and Ginpreet Kaur*

Volume 11, Issue 1, 2022

Published on: 27 July, 2022

Article ID: e070622205682 Pages: 12

DOI: 10.2174/2211556011666220607112528

Price: $65

Abstract

Attention deficit hyperactive disorder or ADHD is a common disorder among children, and if not identified early, it may affect the child’s later life. Pharmacotherapy in ADHD has been linked to the emergence of other emotional disorders. Children who get pharmacological treatment are more likely to continue taking these medications until adulthood, increasing their risk of acquiring other psychological problems. As a result, the majority of ADHD patients are eventually prescribed numerous medicines to manage emotional difficulties as well. Thus, AI tools are seen to be a boon for ADHD patients and clinicians. There have been emerging approaches in using artificial intelligence tools to diagnose and treat ADHD in recent years. Different algorithms and medical devices are used for greater accuracy and precision. The various neural networks detect complex signals in the human brain and analyze them. As it is a neurodevelopmental disorder, AI gives the best tools for proper diagnosis and treatment. Virtual and physical branches of AI are a great help to the patient. This review article focuses on the use of various AI models and tools that employ ADHD symptoms, MRI scans, and EEG signals, using electroencephalogram sensors to monitor brain activity, to help physicians better manage this prevalent neurodevelopmental disorder.

Keywords: Artificial intelligence, attention-deficit hyperactivity disorder, deep learning, inno-sphere, tiimo mood, neurosky.

Graphical Abstract

[1]
N. Institute of Mental Health. Attention-Deficit/Hyperactivity Disorder (ADHD). Basics 2016.
[2]
Danielson ML, Bitsko RH, Ghandour RM, Holbrook JR, Kogan MD, Blumberg SJ. Prevalence of parent-reported adhd diagnosis and associated treatment among U.S. children and adolescents, 2016. J Clin Child Adolesc Psychol 2018; 47(2): 199-212.
[http://dx.doi.org/10.1080/15374416.2017.1417860] [PMID: 29363986]
[3]
Rube DM, Kaur T. Attention-Deficit Hyperactivity Disorder Clinical Child Psychiatry. Third 2012; pp. 152-74.
[http://dx.doi.org/10.1002/9781119962229.ch10]
[4]
Sciberras E, Mulraney M, Silva D, Coghill D. Prenatal risk factors and the etiology of adhd-review of existing evidence. Curr Psychiatry Rep 2017; 19(1): 1.
[http://dx.doi.org/10.1007/s11920-017-0753-2] [PMID: 28091799]
[5]
Breaux R, Dvorsky MR, Marsh NP, et al. Prospective impact of COVID-19 on mental health functioning in adolescents with and without ADHD: Protective role of emotion regulation abilities. J Child Psychol Psychiatry 2021; 62(9): 1132-9.
[http://dx.doi.org/10.1111/jcpp.13382] [PMID: 33543486]
[6]
Miller M, Hinshaw SP. Attention-Deficit/Hyperactivity Disorder (ADHD), encyclopedia of the neurological sciences. In Encyclopedia of the Neurological Sciences 2014; pp. 320-2.
[http://dx.doi.org/10.1016/B978-0-12-385157-4.00431-0]
[7]
Centers for disease control and prevention Attention- Deficit/Hyperactivity Disorder 2018.
[8]
Vogel SWN, Ten Have M, Bijlenga D, de Graaf R, Beekman ATF, Kooij JJS. Distribution of ADHD symptoms, and associated comorbidity, exposure to risk factors and disability: Results from a general population study. Psychiatry Res 2018; 267: 256-65.
[http://dx.doi.org/10.1016/j.psychres.2018.06.017] [PMID: 29940457]
[9]
Mukherjee P, Hartanto T, Iosif AM, et al. Neural basis of working memory in ADHD: Load versus complexity. Neuroimage Clin 2021; 30: 102662.
[http://dx.doi.org/10.1016/j.nicl.2021.102662] [PMID: 34215140]
[10]
Slobodin O, Masalha R. Challenges in ADHD care for ethnic minority children: A review of the current literature. Transcult Psychiatry 2020; 57(3): 468-83.
[http://dx.doi.org/10.1177/1363461520902885] [PMID: 32233772]
[11]
Riegler A, Völkl-Kernstock S, Lesch O, Walter H, Skala K. Attention deficit hyperactivity disorder and substance abuse: An investigation in young Austrian males. J Affect Disord 2017; 217: 60-5.
[http://dx.doi.org/10.1016/j.jad.2017.03.072] [PMID: 28391109]
[12]
Storebø OJ, Pedersen N, Ramstad E, et al. Methylphenidate for Attention Deficit Hyperactivity Disorder (ADHD) in children and adolescents - assessment of adverse events in non-randomised studies. Cochrane Database Syst Rev 2018; 5: CD012069.
[http://dx.doi.org/10.1002/14651858.CD012069.pub2] [PMID: 29744873]
[13]
Pozzi M, Bertella S, Gatti E, et al. Emerging drugs for the treatment of Attention-Deficit Hyperactivity Disorder (ADHD). Expert Opin Emerg Drugs 2020; 25(4): 395-407.
[http://dx.doi.org/10.1080/14728214.2020.1820481] [PMID: 32938246]
[14]
Moriyama TS, Polanczyk GV, Terzi FS, Faria KM, Rohde LA. Psychopharmacology and psychotherapy for the treatment of adults with ADHD-a systematic review of available meta-analyses. CNS Spectr 2013; 18(6): 296-306.
[http://dx.doi.org/10.1017/S109285291300031X] [PMID: 23739183]
[15]
Posner J, Polanczyk GV, Sonuga-Barke E. Attention-deficit hyperactivity disorder. Lancet 2020; 395(10222): 450-62.
[http://dx.doi.org/10.1016/S0140-6736(19)33004-1] [PMID: 31982036]
[16]
Scaglione I. Isabela scaglione honors capstone the efficacy and ethical issues of ADHD medication in young children and alternate methods of management 1 scaglione: The efficacy and ethical issues of ADHD medication in young child. 2021. Available from: https://digitalcommons.sacredheart.edu/acadfest/2021/all/40
[17]
Gómez-González E, Gomez E, Márquez-Rivas J, et al. Artificial intelligence in medicine and healthcare: A review and classification of current and near-future applications and their ethical and social impact arXiv preprint arXiv:2001.09778. 2020. Available from: http://arxiv.org/abs/2001.09778
[18]
Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: Past, present and future. Stroke Vasc Neurol 2017; 2(4): 230-43.
[http://dx.doi.org/10.1136/svn-2017-000101] [PMID: 29507784]
[19]
Tenev A, Markovska-Simoska S, Kocarev L, Pop-Jordanov J, Müller A, Candrian G. Machine learning approach for classification of ADHD adults. Int J Psychophysiol 2014; 93(1): 162-6.
[http://dx.doi.org/10.1016/j.ijpsycho.2013.01.008] [PMID: 23361114]
[20]
Ellahham S, Ellahham N, Simsekler MCE. Application of artificial intelligence in the health care safety context: Opportunities and challenges. Am J Med Qual 2020; 35(4): 341-8.
[http://dx.doi.org/10.1177/1062860619878515] [PMID: 31581790]
[21]
Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism 2017; 69S: S36-40.
[http://dx.doi.org/10.1016/j.metabol.2017.01.011] [PMID: 28126242]
[22]
Bhongale MP, Vyas R. Role of BioJava in the Department of Bioinformatics Tools. In Advances in Bioengineering; Springer, Singapore. 2020; pp. 29-49.
[http://dx.doi.org/10.1007/978-981-15-2063-1]
[23]
Snapshot of the Indian market Key consideration. Life science 2019.
[24]
Muthuswamy V. Ethical issues in artificial intelligence in health care (forum for ethics review committees in india) what is artificial intelligence. J Chem Inf Model 2019; 35: 13-7. Available from: https://www.itu.int/en/ITU-T/Workshops-and-Seminars/ai4h/201911/Documents/S4_Vasantha_Muthuswamy_Presentation.pdf
[25]
Navalyal GU, Gavas RD. A dynamic attention assessment and enhancement tool using computer graphics. Human-Centric Computing and Information Sciences 2014; 4(1): 11.
[http://dx.doi.org/10.1186/s13673-014-0011-0]
[26]
Patel UK, Anwar A, Saleem S, et al. Artificial intelligence as an emerging technology in the current care of neurological disorders. J Neurol 2019; 268(5): 1623-42.
[http://dx.doi.org/10.1007/s00415-019-09518-3] [PMID: 31451912]
[27]
Kuang D, He L. Classification on ADHD with deep learning. In 2014 IEEE International Conference on Cloud Computing and Big Data 2014; pp. 27-32.
[http://dx.doi.org/10.1109/CCBD.2014.42]
[28]
Khullar V, Salgotra K, Singh HP, Sharma DP. Deep learning-based binary classification of ADHD using resting state MR images. Augment Hum Res 2021; 6(1): 5.
[http://dx.doi.org/10.1007/s41133-020-00042-y]
[29]
Dark C, Homman-Ludiye J, Bryson-Richardson RJ. The role of ADHD associated genes in neurodevelopment. Dev Biol 2018; 438(2): 69-83.
[http://dx.doi.org/10.1016/j.ydbio.2018.03.023] [PMID: 29608877]
[30]
Duda M, Ma R, Haber N, Wall DP. Use of machine learning for behavioral distinction of autism and ADHD. Transl Psychiatry 2016; 6(2): e732.
[http://dx.doi.org/10.1038/tp.2015.221] [PMID: 26859815]
[31]
Paloyelis Y, Mehta MA, Kuntsi J, Asherson P. Functional MRI in ADHD: A systematic literature review. Expert Rev Neurother 2007; 7(10): 1337-56.
[http://dx.doi.org/10.1586/14737175.7.10.1337] [PMID: 17939771]
[32]
Cherkasova MV, Hechtman L. Neuroimaging in attention-deficit hyperactivity disorder: Beyond the frontostriatal circuitry. Can J Psychiatry 2009; 54(10): 651-64.
[33]
Anitha S, Thomas Geroge S. ADHD classification from FMRI data using fine tunining in SVM. J Phys 2021; 1937(1): 012014.
[http://dx.doi.org/10.1088/1742-6596/1937/1/012014]
[34]
Bledsoe JC, Xiao C, Chaovalitwongse A, et al. Diagnostic classification of ADHD versus control: Support vector machine classification using brief neuropsychological assessment. J Atten Disord 2020; 24(11): 1547-56.
[http://dx.doi.org/10.1177/1087054716649666] [PMID: 27231214]
[35]
Fink A, Schwab D, Papousek I. Sensitivity of EEG upper alpha activity to cognitive and affective creativity interventions. Int J Psychophysiol 2011; 82(3): 233-9.
[http://dx.doi.org/10.1016/j.ijpsycho.2011.09.003] [PMID: 21930162]
[36]
Aceves-Fernandez MA. Methodology proposal of ADHD classification of children based on cross recurrence plots. Nonlinear Dyn 2021; 104(2): 1491-505.
[http://dx.doi.org/10.1007/s11071-021-06336-z]
[37]
Vashistha R, Yadav D, Chhabra D, Shukla P. Artificial intelligence integration for neurodegenerative disorders. Elsevier Inc. 2018.
[http://dx.doi.org/10.1016/B978-0-12-809556-0.00005-8]
[38]
Mohamed SMH, Butzbach M, Fuermaier ABM, et al. Basic and complex cognitive functions in Adult ADHD. PLoS One 2021; 16(9): e0256228.
[http://dx.doi.org/10.1371/journal.pone.0256228] [PMID: 34473722]
[39]
Sachnev V, Suresh S. An ADHD diagnostic approach based on binary-coded genetic algorithm and extreme learning machine. J Comput Sci Eng 2016; 10(4): 111-7.
[http://dx.doi.org/10.5626/JCSE.2016.10.4.111]
[40]
Chang YS, Chui CH. Integrating artificial intelligence into steam education. In International Cognitive Cities Conference; Springer, Singapore. 2019; pp. 469-74.
[http://dx.doi.org/10.1007/978-981-15-6113-9_52]
[41]
Shuaib A, Arian H, Shuaib A. The increasing role of artificial intelligence in health care: Will robots replace doctors in the future? Int J Gen Med 2020; 13: 891-6.
[http://dx.doi.org/10.2147/IJGM.S268093] [PMID: 33116781]
[42]
Rajeswari J, Jagannath M. Advances in biomedical signal and image processing - A systematic review. Inform Med Unlocked 2017; 8: 13-9.
[http://dx.doi.org/10.1016/j.imu.2017.04.002]
[43]
Ameis SH, Lerch JP, Taylor MJ, et al. A diffusion tensor imaging study in children with ADHD, autism spectrum disorder, OCD, and matched controls: Distinct and non-distinct white matter disruption and dimensional brain-behavior relationships. Am J Psychiatry 2016; 173(12): 1213-22.
[http://dx.doi.org/10.1176/appi.ajp.2016.15111435] [PMID: 27363509]
[44]
Bertel LB, Nørlem HL, Azari M. Supporting self-efficacy in children with ADHD through ai-supported self-monitoring: Initial findings from a case study on tiimood. CEUR Workshop Proc 2020; 2629: 1-4.
[45]
Aps T. Integrating end-user’s self- assessment module in ADHD assistive technologies for the continuous enhancement of nonmedical therapies Periodic Reporting for period 1 - Tiimood (Tiimo mood: Integrating end-user’s self-assessment module i). 2019; pp. 1-4.
[46]
Zheng Y, Li R, Li S, Zhang Y, Yang S, Ning H. A review on serious games for ADHD 2021. Available from: http://arxiv.org/abs/2105.02970
[47]
Pan MR, Huang F, Zhao MJ, Wang YF, Wang YF, Qian QJ. A comparison of efficacy between Cognitive Behavioral Therapy (CBT) and CBT combined with medication in adults with Attention-Deficit/Hyperactivity Disorder (ADHD). Psychiatry Res 2019; 279: 23-33.
[http://dx.doi.org/10.1016/j.psychres.2019.06.040] [PMID: 31280035]
[48]
Angelakis E, Lubar JF, Stathopoulou S, Kounios J. Peak alpha frequency: An electroencephalographic measure of cognitive preparedness. Clin Neurophysiol 2004; 115(4): 887-97.
[http://dx.doi.org/10.1016/j.clinph.2003.11.034] [PMID: 15003770]
[49]
Pettersson R, Söderström S, Edlund-Söderström K, Nilsson KW. Internet-based cognitive behavioral therapy for adults with ADHD in outpatient psychiatric care. J Atten Disord 2017; 21(6): 508-21.
[http://dx.doi.org/10.1177/1087054714539998] [PMID: 24970720]
[50]
Ging-Jehli NR, Ratcliff R, Arnold LE. Improving neurocognitive testing using computational psychiatry-A systematic review for ADHD. Psychol Bull 2021; 147(2): 169-231.
[http://dx.doi.org/10.1037/bul0000319] [PMID: 33370129]
[51]
Jang S, Kim JJ, Kim SJ, Hong J, Kim S, Kim E. Mobile app-based chatbot to deliver cognitive behavioral therapy and psychoeducation for adults with attention deficit: A development and feasibility/usability study. Int J Med Inform 2021; 150: 104440.
[http://dx.doi.org/10.1016/j.ijmedinf.2021.104440] [PMID: 33799055]
[52]
Jansen I, Philipsen A, Dalin D, Wiesmeier IK, Maurer C. Postural instability in adult ADHD - A pilot study. Gait Posture 2019; 67: 284-9.
[http://dx.doi.org/10.1016/j.gaitpost.2018.10.016] [PMID: 30391751]
[53]
Strauss BI, An P, Innosphere T, Founder I, Shacour CEOR. A permanent solution for ADHD sufferers ? 2019. 2019-21.
[54]
Innosphere overview Ehab Shakour 1, 1 Innosphere 18 n.d.
[55]
Segato A, Marzullo A, Calimeri F, De Momi E. Artificial intelligence for brain diseases: A systematic review. APL Bioeng 2020; 4(4): 041503.
[http://dx.doi.org/10.1063/5.0011697] [PMID: 33094213]
[56]
Belić M, Bobić V, Badža M, Šolaja N, Đurić-Jovičić M, Kostić VS. Artificial intelligence for assisting diagnostics and assessment of Parkinson’s disease-A review. Clin Neurol Neurosurg 2019; 184: 105442.
[http://dx.doi.org/10.1016/j.clineuro.2019.105442] [PMID: 31351213]
[57]
Ahmadi A, Kashefi M, Shahrokhi H, Nazari MA. Computer aided diagnosis system using deep convolutional neural networks for ADHD subtypes. Biomed Signal Process Control 2021; 63: 102227.
[http://dx.doi.org/10.1016/j.bspc.2020.102227]
[58]
Bélanger SA, Andrews D, Gray C, Korczak D. ADHD in children and youth: Part 1-Etiology, diagnosis, and comorbidity. Paediatr Child Health 2018; 23(7): 447-53.
[http://dx.doi.org/10.1093/pch/pxy109] [PMID: 30681669]
[59]
Mechler K, Banaschewski T, Hohmann S, Häge A. Evidence-based pharmacological treatment options for ADHD in children and adolescents. Pharmacol Ther 2021; 107940.
[http://dx.doi.org/10.1016/j.pharmthera.2021.107940] [PMID: 34174276]
[60]
Amiri A, Torabi Parizi G, Kousha M, et al. Changes in plasma brain-derived neurotrophic factor (BDNF) levels induced by methylphenidate in children with attention deficit-hyperactivity disorder (ADHD). Prog Neuropsychopharmacol Biol Psychiatry 2013; 47: 20-4.
[http://dx.doi.org/10.1016/j.pnpbp.2013.07.018] [PMID: 23933054]
[61]
Faraone SV, McBurnett K, Sallee FR, Steeber J, López FA. Guanfacine extended release: A novel treatment for attention-deficit/hyperactivity disorder in children and adolescents. Clin Ther 2013; 35(11): 1778-93.
[http://dx.doi.org/10.1016/j.clinthera.2013.09.005] [PMID: 24139092]
[62]
Bymaster FP, Katner JS, Nelson DL, et al. Atomoxetine increases extracellular levels of norepinephrine and dopamine in prefrontal cortex of rat: A potential mechanism for efficacy in attention deficit/hyperactivity disorder. Neuropsychopharmacology 2002; 27(5): 699-711.
[http://dx.doi.org/10.1016/S0893-133X(02)00346-9] [PMID: 12431845]
[63]
Of H, Information P, Approval IUS, et al. No Title 2002; 1-19.
[64]
Carucci S, Balia C, Gagliano A, et al. Long term methylphenidate exposure and growth in children and adolescents with ADHD. A systematic review and meta-analysis. Neurosci Biobehav Rev 2021; 120: 509-25.
[http://dx.doi.org/10.1016/j.neubiorev.2020.09.031] [PMID: 33080250]
[65]
Nasser A, Liranso T, Adewole T, et al. A phase iii, randomized, placebo-controlled trial to assess the efficacy and safety of once-daily spn-812 (viloxazine extended-release) in the treatment of attention-deficit/hyperactivity disorder in school-age children. Clin Ther 2020; 42(8): 1452-66.
[http://dx.doi.org/10.1016/j.clinthera.2020.05.021] [PMID: 32723670]
[66]
Milham PM, Damien F, Maarten M, Stewart HM. A model to advance the translational potential of neuroimaging in clinical neuroscience. Front Syst Neurosci 2012; 6: 62.
[http://dx.doi.org/10.3389/fnsys.2012.00062] [PMID: 22973200]
[67]
de Silva S, Dayarathna SU, Ariyarathne G, Meedeniya D, Jayarathna S. FMRI feature extraction model for ADHD classification using convolutional neural network. Int J E-Health Med Commun 2021; 12(1): 81-105.
[http://dx.doi.org/10.4018/IJEHMC.2021010106]
[68]
Tachmazidis I, Chen T, Adamou M, Antoniou G. A hybrid AI approach for supporting clinical diagnosis of Attention Deficit Hyperactivity Disorder (ADHD) in adults. Health Inf Sci Syst 2020; 9(1): 1-9.
[http://dx.doi.org/10.1007/s13755-020-00123-7] [PMID: 33235709]
[69]
Chen Y, Tang Y, Wang C, Liu X, Zhao L, Wang Z. ADHD classification by dual subspace learning using resting-state functional connectivity. Artif Intell Med 2020; 103: 101786.
[http://dx.doi.org/10.1016/j.artmed.2019.101786] [PMID: 32143793]

Rights & Permissions Print Cite
Article Metrics
18
1
© 2024 Bentham Science Publishers | Privacy Policy