Scoping Review sobre el uso de la Inteligencia Artificial (AI) en el diagnóstico del Déficit de Atención e Hiperactividad (TDAH) en menores de 18 años, durante los años 2019 a 2024
Introducción: El trastorno por déficit de atención e hiperactividad (TDAH) presenta una prevalencia del 3-5 % a nivel mundial. Sus manifestaciones clínicas suelen aparecer antes de los 12 años y constituye una de las entidades más frecuentes en la práctica de la psiquiatría infantil. El diagnóstico...
- Autores:
-
Aldas Ávila, Daniela Alejandra
Ballestero Galeano, Nalfer Francisco
- Tipo de recurso:
- https://purl.org/coar/resource_type/c_7a1f
- Fecha de publicación:
- 2025
- Institución:
- Universidad El Bosque
- Repositorio:
- Repositorio U. El Bosque
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unbosque.edu.co:20.500.12495/15042
- Acceso en línea:
- https://hdl.handle.net/20.500.12495/15042
- Palabra clave:
- Revisión de alcance
Diagnóstico
Déficit de Atención e Hiperactividad
Niñez
Inteligencia Artificial
Aprendizaje Automatico
Scoping Review
Diagnosis
Attention deficit hyperactivity disorder (ADHD)
Child
Artificial intelligence (AI)
Machine learning
WS 350
- Rights
- License
- Attribution-NonCommercial-ShareAlike 4.0 International
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Repositorio U. El Bosque |
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| dc.title.none.fl_str_mv |
Scoping Review sobre el uso de la Inteligencia Artificial (AI) en el diagnóstico del Déficit de Atención e Hiperactividad (TDAH) en menores de 18 años, durante los años 2019 a 2024 |
| dc.title.translated.none.fl_str_mv |
Scoping Review on the Use of Artificial Intelligence (AI) in the Diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) in Individuals Under 18 Years of Age, 2019–2024 |
| title |
Scoping Review sobre el uso de la Inteligencia Artificial (AI) en el diagnóstico del Déficit de Atención e Hiperactividad (TDAH) en menores de 18 años, durante los años 2019 a 2024 |
| spellingShingle |
Scoping Review sobre el uso de la Inteligencia Artificial (AI) en el diagnóstico del Déficit de Atención e Hiperactividad (TDAH) en menores de 18 años, durante los años 2019 a 2024 Revisión de alcance Diagnóstico Déficit de Atención e Hiperactividad Niñez Inteligencia Artificial Aprendizaje Automatico Scoping Review Diagnosis Attention deficit hyperactivity disorder (ADHD) Child Artificial intelligence (AI) Machine learning WS 350 |
| title_short |
Scoping Review sobre el uso de la Inteligencia Artificial (AI) en el diagnóstico del Déficit de Atención e Hiperactividad (TDAH) en menores de 18 años, durante los años 2019 a 2024 |
| title_full |
Scoping Review sobre el uso de la Inteligencia Artificial (AI) en el diagnóstico del Déficit de Atención e Hiperactividad (TDAH) en menores de 18 años, durante los años 2019 a 2024 |
| title_fullStr |
Scoping Review sobre el uso de la Inteligencia Artificial (AI) en el diagnóstico del Déficit de Atención e Hiperactividad (TDAH) en menores de 18 años, durante los años 2019 a 2024 |
| title_full_unstemmed |
Scoping Review sobre el uso de la Inteligencia Artificial (AI) en el diagnóstico del Déficit de Atención e Hiperactividad (TDAH) en menores de 18 años, durante los años 2019 a 2024 |
| title_sort |
Scoping Review sobre el uso de la Inteligencia Artificial (AI) en el diagnóstico del Déficit de Atención e Hiperactividad (TDAH) en menores de 18 años, durante los años 2019 a 2024 |
| dc.creator.fl_str_mv |
Aldas Ávila, Daniela Alejandra Ballestero Galeano, Nalfer Francisco |
| dc.contributor.advisor.none.fl_str_mv |
Toledo Arenas, José Daniel Franco Zuluaga , Álvaro |
| dc.contributor.author.none.fl_str_mv |
Aldas Ávila, Daniela Alejandra Ballestero Galeano, Nalfer Francisco |
| dc.contributor.orcid.none.fl_str_mv |
Aldas Ávila, Daniela Alejandra [0009-0004-1975-4643] Ballestero Galeano, Nalfer Francisco [0009-0003-9889-4531] |
| dc.subject.none.fl_str_mv |
Revisión de alcance Diagnóstico Déficit de Atención e Hiperactividad Niñez Inteligencia Artificial Aprendizaje Automatico |
| topic |
Revisión de alcance Diagnóstico Déficit de Atención e Hiperactividad Niñez Inteligencia Artificial Aprendizaje Automatico Scoping Review Diagnosis Attention deficit hyperactivity disorder (ADHD) Child Artificial intelligence (AI) Machine learning WS 350 |
| dc.subject.keywords.none.fl_str_mv |
Scoping Review Diagnosis Attention deficit hyperactivity disorder (ADHD) Child Artificial intelligence (AI) Machine learning |
| dc.subject.nlm.none.fl_str_mv |
WS 350 |
| description |
Introducción: El trastorno por déficit de atención e hiperactividad (TDAH) presenta una prevalencia del 3-5 % a nivel mundial. Sus manifestaciones clínicas suelen aparecer antes de los 12 años y constituye una de las entidades más frecuentes en la práctica de la psiquiatría infantil. El diagnóstico se basa principalmente en la evaluación clínica, lo que genera elevada variabilidad intersubjetiva y frecuente sub y sobrediagnóstico. Para reducir este problema, se han propuesto diversas pruebas, herramientas y biomarcadores orientados a una valoración más objetiva. Objetivo: Realizar una revisión de alcance sobre la implementación de métodos basados en inteligencia artificial (IA) en el diagnóstico del TDAH. Métodos: Llevamos a cabo una revisión de la literatura indexada y de literatura "gris", en base de datos como Embase, PubMed y Ovid, siguiendo la guía PRISMA para revisiones de alcance (PRISMA-ScR). Llevamos a cabo un análisis descriptivo y crítico de la literatura, enfatizando en las tendencias, lagunas de conocimiento y líneas propuestas de investigación. Resultados: 242 referencias cumplieron los criterios de inclusión. Detallamos un aumento sostenido de la investigación sobre IA aplicada al diagnóstico del TDAH en el periodo estudiado, principalmente en Estados Unidos y China. En la mayoría de los estudios los autores emplearon algoritmos de machine learning (ML) sobre muestras de electroencefalografía (EEG) (35 %) y neuroimágenes (32 %). Las métricas de desempeño de los modelos oscilaron entre el 80 % y el 100 % para diferenciar casos de controles. Pese a su potencial, evidenciamos limitaciones metodológicas y prácticas que restringen su adopción generalizada de la aplicación de la IA en el diagnóstico del TDAH. Conclusiones: En la actualidad, la IA no sustituye la entrevista clínica, el análisis multidimensional ni la evaluación contextual realizada por especialistas humanos; sigue siendo una herramienta complementaria al juicio clínico. |
| publishDate |
2025 |
| dc.date.accessioned.none.fl_str_mv |
2025-07-23T16:38:52Z |
| dc.date.available.none.fl_str_mv |
2025-07-23T16:38:52Z |
| dc.date.issued.none.fl_str_mv |
2025-06 |
| dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_7a1f |
| dc.type.local.spa.fl_str_mv |
Tesis/Trabajo de grado - Monografía - Especialización |
| dc.type.coar.none.fl_str_mv |
https://purl.org/coar/resource_type/c_7a1f |
| dc.type.driver.none.fl_str_mv |
info:eu-repo/semantics/bachelorThesis |
| dc.type.coarversion.none.fl_str_mv |
https://purl.org/coar/version/c_ab4af688f83e57aa |
| format |
https://purl.org/coar/resource_type/c_7a1f |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12495/15042 |
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instname:Universidad El Bosque |
| dc.identifier.reponame.spa.fl_str_mv |
reponame:Repositorio Institucional Universidad El Bosque |
| dc.identifier.repourl.none.fl_str_mv |
repourl:https://repositorio.unbosque.edu.co |
| url |
https://hdl.handle.net/20.500.12495/15042 |
| identifier_str_mv |
instname:Universidad El Bosque reponame:Repositorio Institucional Universidad El Bosque repourl:https://repositorio.unbosque.edu.co |
| dc.language.iso.fl_str_mv |
spa |
| language |
spa |
| dc.relation.references.none.fl_str_mv |
Ahire, N., Awale, R. N., & Wagh, A. (2023). Comprehensive review of EEG data classification techniques for ADHD detection using machine learning and deep learning. In Romanian Journal of Pediatrics (Vol. 72, Issue 2, pp. 57–66). Amaltea Medical Publishing House. https://doi.org/10.37897/RJP.2023.2.1 American Psychiatric Association. (2013). DSM-5. In Madrid (5th ed.). Editorial Médica Panamericana. https://doi.org/10.1176/appi.books.9780890425596.744053 Amigo Martins, L., & Romina Leardi, M. (2025). Attention Deficit Hyperactivity Disorder: Late diagnosis and its psychosocial and functional consequences. South Health and Policy, 4, 206. https://doi.org/10.56294/shp2025206 Artificial Intelligence/Machine Learning-enabled Working Group. (2024). Good machine learning practice for medical device development: Guiding principles AUTHORING GROUP Artificial Intelligence/Machine Learning-enabled Working Group Preface. Berrezueta-Guzman, J., Krusche, S., Serpa-Andrade, L., & Martín-Ruiz, M. L. (2023). Artificial Vision Algorithm for Behavior Recognition in Children with ADHD in a Smart Home Environment. Lecture Notes in Networks and Systems, 542 LNNS, 661–671. https://doi.org/10.1007/978-3-031-16072-1_47 Biederman, J., Faraone, S. V., Spencer, T. J., Mick, E., Monuteaux, M. C., & Aleardi, M. (2006). Functional impairments in adults with self-reports of diagnosed ADHD: A controlled study of 1001 adults in the community. In Journal of Clinical Psychiatry (Vol. 67, Issue 4). https://doi.org/10.4088/JCP.v67n0403 Celis, M. de. (2014). Volviendo a la Normalidad. La Invención del TDAH y del Trastorno Bipolar Infantil. Clínica Contemporánea, 5(3). https://doi.org/10.5093/cc2014a22 Chang, L. Y., Wang, M. Y., & Tsai, P. S. (2016). Diagnostic accuracy of Rating Scales for attentiondeficit/hyperactivity disorder: A meta-analysis. Pediatrics, 137(3). https://doi.org/10.1542/peds.2015-2749 Chen, C. C., Wu, E. H. K., Chen, Y. Q., Tsai, H. J., Chung, C. R., & Yeh, S. C. (2023). Neuronal Correlates of Task Irrelevant Distractions Enhance the Detection of Attention Deficit/Hyperactivity Disorder. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, 1302–1310. https://doi.org/10.1109/TNSRE.2023.3241649 Chen, H., Chen, W., Song, Y., Sun, L., & Li, X. (2019). EEG characteristics of children with attentiondeficit/hyperactivity disorder. Neuroscience, 406, 444–456. https://doi.org/10.1016/j.neuroscience.2019.03.048 Chen, H., Yang, Y., Odisho, D., Wu, S., Yi, C., & Oliver, B. G. (2023). Can biomarkers be used to diagnose attention deficit hyperactivity disorder? Frontiers in Psychiatry, 14. https://doi.org/10.3389/FPSYT.2023.1026616/FULL Chen, T., Tachmazidis, I., Batsakis, S., Adamou, M., Papadakis, E., & Antoniou, G. (2023). Diagnosing attention-deficit hyperactivity disorder (ADHD) using artificial intelligence: a clinical study in the UK. Frontiers in Psychiatry, 14. https://doi.org/10.3389/fpsyt.2023.1164433 CIE-11. (2023). Clasificación Internacional de Enfermedades, 11.a revisión (CIE 11). Oms. Cornejo Ochoa, J. W., Osío Uribe, Ó., Sánchez Mosquera, Y., Carrizosa Moog, J., Sánchez Aldana, G., Grisales Romero, H., Castillo Parra, H., & Holguín Acosta, J. (2005). Prevalencia del trastorno por déficit de atención-hiperactividad en niños y adolescentes colombianos. Revista de Neurología, 40(12), 716. https://doi.org/10.33588/rn.4012.2004569 Crichton A. (1978). An inquiry into the nature and origin of mental derangement: comprehending a concise system of the physiology and pathology of the human mind and a history of the passions and their effects. De Lacy, N., & Ramshaw, M. J. (2023). Selectively predicting the onset of ADHD, oppositional defiant disorder, and conduct disorder in early adolescence with high accuracy. Frontiers in Psychiatry, 14. https://doi.org/10.3389/fpsyt.2023.1280326 De Souza, I., Mattos, P., Pina, C., & Fortes, D. (2008). ADHD: The impact when not diagnosed. Jornal Brasileiro de Psiquiatria, 57(2). https://doi.org/10.1590/s0047-20852008000200010 Duda, M., Ma, R., Haber, N., & Wall, D. P. (2016). Use of machine learning for behavioral distinction of autism and ADHD. Translational Psychiatry, 6(2). https://doi.org/10.1038/tp.2015.221 Eslami, T., Almuqhim, F., Raiker, J. S., & Saeed, F. (2021). Machine Learning Methods for Diagnosing Autism Spectrum Disorder and Attention- Deficit/Hyperactivity Disorder Using Functional and Structural MRI: A Survey. In Frontiers in Neuroinformatics (Vol. 14). Frontiers Media S.A. https://doi.org/10.3389/fninf.2020.575999 Espinet, S. D., Graziosi, G., Toplak, M. E., Hesson, J., & Minhas, P. (2022). A Review of Canadian Diagnosed ADHD Prevalence and Incidence Estimates Published in the Past Decade. In Brain Sciences (Vol. 12, Issue 8). https://doi.org/10.3390/brainsci12081051 Faraone, S. V., Biederman, J., & Mick, E. (2006). The age-dependent decline of attention deficit hyperactivity disorder: A meta-analysis of follow-up studies. In Psychological Medicine (Vol. 36,Issue 2). https://doi.org/10.1017/S003329170500471X Faraone, S. V., Bonvicini, C., & Scassellati, C. (2014). Biomarkers in the Diagnosis of ADHD – Promising Directions. In Current Psychiatry Reports (Vol. 16, Issue 11). https://doi.org/10.1007/s11920-014-0497-1 Faraone, S. V., & Larsson, H. (2019). Genetics of attention deficit hyperactivity disorder. In Molecular Psychiatry (Vol. 24, Issue 4). https://doi.org/10.1038/s41380-018-0070-0 Flores, M. G. (2023). Industry perspective of artificial intelligence in medicine and surgery. In Intelligence-Based Cardiology and Cardiac Surgery: Artificial Intelligence and Human Cognition in Cardiovascular Medicine. https://doi.org/10.1016/B978-0-323-90534-3.00031-7 Furlong, S., Cohen, J. R., Hopfinger, J., Snyder, J., Robertson, M. M., & Sheridan, M. A. (2021). Resting-state EEG Connectivity in Young Children with ADHD. Journal of Clinical Child and Adolescent Psychology, 50(6), 746–762. https://doi.org/10.1080/15374416.2020.1796680 Geneau, M. (2022a). L’intelligence artificielle au service de la santé mentale. Geneau, M. (2022b). L’intelligence artificielle au service de la santé mentale. Université LAVAL. Giacobini, M. B., Ahnemark, E., Medin, E., Freilich, J., Andersson, M., Ma, Y., & Ginsberg, Y. (2023). Epidemiology, Treatment Patterns, Comorbidities, and Concomitant Medication in Patients with ADHD in Sweden: A Registry-Based Study (2018–2021). Journal of Attention Disorders, 27(12). https://doi.org/10.1177/10870547231177221 Good machine learning practice for medical device development: Guiding principles AUTHORING GROUP Artificial Intelligence/Machine Learning-enabled Working Group Preface. (2023) Gualtieri, C. T., & Johnson, L. G. (2005a). ADHD: Is Objective Diagnosis Possible? Psychiatry (Edgmont (Pa. : Township)), 2(11). Gualtieri, C. T., & Johnson, L. G. (2005b). ADHD: Is Objective Diagnosis Possible? Psychiatry (Edgmont (Pa. : Township)), 2(11). He, Y., Wang, X., Yang, Z., Xue, L., Chen, Y., Ji, J., Wan, F., Mukhopadhyay, S. C., Men, L., Tong, M. C. F., Li, G., & Chen, S. (2023). Classification of attention deficit/hyperactivity disorder based on EEG signals using a EEG-Transformer model. Journal of Neural Engineering, 20(5). https://doi.org/10.1088/1741-2552/acf7f5 Hintze, A. (2016, November 18). Understanding the Four Types of AI, From Reactive Robots to Self-Aware Beings. Observer. Hoogman, M., Bralten, J., Hibar, D. P., Mennes, M., Zwiers, M. P., Schweren, L. S. J., van Hulzen, K.J. E., Medland, S. E., Shumskaya, E., Jahanshad, N., Zeeuw, P. de, Szekely, E., Sudre, G., Wolfers, T., Onnink, A. M. H., Dammers, J. T., Mostert, J. C., Vives-Gilabert, Y., Kohls, G., … Franke, B. (2017). Subcortical brain volume differences of participants with ADHD across the lifespan: an ENIGMA collaboration. The Lancet. Psychiatry, 4(4). IBM. (n.d.). ¿Qué es la inteligencia artificial (IA)? Retrieved April 9, 2024, from https://www.ibm.com/mx-es/topics/artificial-intelligence Kautzky, A., Vanicek, T., Philippe, C., Kranz, G. S., Wadsak, W., Mitterhauser, M., Hartmann, A., Hahn, A., Hacker, M., Rujescu, D., Kasper, S., & Lanzenberger, R. (2020). Machine learning classification of ADHD and HC by multimodal serotonergic data. Translational Psychiatry, 10(1). https://doi.org/10.1038/s41398-020-0781-2 Kazda, L., Bell, K., Thomas, R., McGeechan, K., Sims, R., & Barratt, A. (2021). Overdiagnosis of Attention-Deficit/Hyperactivity Disorder in Children and Adolescents. JAMA Network Open, 4(4),e215335. https://doi.org/10.1001/jamanetworkopen.2021.5335 Kulkarni, V., Nemade, B., Patel, S., Patel, K., & Velpula, S. (2024). A short report on ADHD detection using convolutional neural networks. Frontiers in Psychiatry, 15. https://doi.org/10.3389/fpsyt.2024.1426155 Kuntsi, J., Larsson, H., Deng, Q., Lichtenstein, P., & Chang, Z. (2022). The Combined Effects of Young Relative Age and Attention-Deficit/Hyperactivity Disorder on Negative Long-term Outcomes. Journal of the American Academy of Child and Adolescent Psychiatry, 61(2). https://doi.org/10.1016/j.jaac.2021.07.002 Latifi, B., Amini, A., & Motie Nasrabadi, A. (2024). Siamese based deep neural network for ADHD detection using EEG signal. Computers in Biology and Medicine, 182. https://doi.org/10.1016/j.compbiomed.2024.109092 Lee, D. W., Lee, S. H., Ahn, D. H., Lee, G. H., Jun, K., & Kim, M. S. (2023). Development of a Multiple RGB-D Sensor System for ADHD Screening and Improvement of Classification Performance Using Feature Selection Method. Applied Sciences (Switzerland), 13(5). https://doi.org/10.3390/app13052798 Lin, H., Haider, S. P., Kaltenhauser, S., Mozayan, A., Malhotra, A., Constable, R. T., Scheinost, D., Ment, L. R., Konrad, K., & Payabvash, S. (2023). Population level multimodal neuroimaging correlates of attention-deficit hyperactivity disorder among children. Frontiers in Neuroscience, 17. https://doi.org/10.3389/fnins.2023.1138670 Liu, Z., Li, J., Zhang, Y., Wu, D., Huo, Y., Yang, J., Zhang, M., Dong, C., Jiang, L., Sun, R., Zhou, R., Li, F., Yu, X., Zhu, D., Guo, Y., & Chen, J. (2024). Auxiliary Diagnosis of Children With Attention-Deficit/Hyperactivity Disorder Using Eye Tracking and Digital Biomarkers: Case-Control Study. JMIR Mhealth and Uhealth, 12, e58927. https://doi.org/10.2196/58927 Lucía Caselles-Pina, Alejandro Quesada-López, Eva María Garzón Hernández, David Delgado-Gómez, & Aaron, S. (2023). A systematic review on the application of machine learning models in psychometric questionnaires for the diagnosis of attention deficit hyperactivity disorder. European Journal of Neuroscience, 60(62), 4115–4127. https://doi.org/10.13039/501100011033 Lynch, S., McDonnell, T., Leahy, D., Gavin, B., & McNicholas, F. (2023). Prevalence of mental health disorders in children and adolescents in the Republic of Ireland: A systematic review. In Irish Journal of Psychological Medicine (Vol. 40, Issue 1). https://doi.org/10.1017/ipm.2022.46 Manjur, S. M., Diaz, L. R. M., Lee, I. O., Skuse, D. H., Thompson, D. A., Marmolejos-Ramos, F., Constable, P. A., & Posada-Quintero, H. F. (2024). Detecting Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder Using Multimodal Time-Frequency Analysis with Machine Learning Using the Electroretinogram from Two Flash Strengths. Journal of Autism and Developmental Disorders. https://doi.org/10.1007/s10803-024-06290-w McCarthy, J. (1998). WHAT IS ARTIFICIAL INTELLIGENCE. https://api.semanticscholar.org/CorpusID:260698276 Miodovnik, A., Harstad, E., Sideridis, G., & Huntington, N. (2015). Timing of the diagnosis of attention-deficit/hyperactivity disorder and autism spectrum disorder. Pediatrics, 136(4). https://doi.org/10.1542/peds.2015-1502 Misgar, M. M., & Bhatia, M. P. S. (2024). Advancing ADHD diagnosis: using machine learning for unveiling ADHD patterns through dimensionality reduction on IoMT actigraphy signals. International Journal of Information Technology (Singapore). https://doi.org/10.1007/s41870-024-01895-x National Institute for Health and Care Excellence. (2008). Attention deficit hyperactivity activity disorder : diagnosis and management. NICE Guideline, September National Institute for Health and Care Excellence. (2024). Digital technologies for assessing attention deficit hyperactivity disorder (ADHD) Diagnostics guidance. www.nice.org.uk/guidance/dg60 National Resource Center on ADHD. (2017). aboutADHD. Neuroimaging Informatics Tools and Resources Clearinghouse. (2011). La muestra del ADHD-200. O’Mahony, N., Florentino-Liano, B., Carballo, J. J., Baca-García, E., & Rodríguez, A. A. (2014). Objective diagnosis of ADHD using IMUs. Medical Engineering and Physics, 36(7). https://doi.org/10.1016/j.medengphy.2014.02.023 Organización Mundial de la salud. (2021). Ethics and governance of artificial intelligence for health: WHO guidance. OMS, 1. Palacios-Cruz, L., de la Peña, F., Valderrama, A., Patiño, R., Portugal, S. P. C., & Ulloa, R. E. (2011). Conocimientos, creencias y actitudes en padres mexicanos acerca del trastorno por déficit de atención con hiperactividad (TDAH). Salud Mental, 34(2). Pereira-Sanchez, V., & Castellanos, F. X. (2021). Neuroimaging in attention-deficit/hyperactivity disorder. In Current Opinion in Psychiatry (Vol. 34, Issue 2). https://doi.org/10.1097/YCO.0000000000000669 Periyasamy, R., Vibashan, V., Varghese, G., & Aleem, M. (2021a). Machine Learning Techniques for the Diagnosis of Attention-Deficit/Hyperactivity Disorder from Magnetic Resonance Imaging: A Concise Review. In Neurology India (Vol. 69, Issue 6). https://doi.org/10.4103/0028-3886.333520 Periyasamy, R., Vibashan, V., Varghese, G., & Aleem, M. (2021b). Machine Learning Techniques for the Diagnosis of Attention-Deficit/Hyperactivity Disorder from Magnetic Resonance Imaging: A Concise Review. In Neurology India (Vol. 69, Issue 6, pp. 1518–1523). Wolters Kluwer Medknow Publications. https://doi.org/10.4103/0028-3886.333520 Periyasamy, R., Vibashan, V., Varghese, G., & Aleem, M. (2021c). Machine Learning Techniques for the Diagnosis of Attention-Deficit/Hyperactivity Disorder from Magnetic Resonance Imaging: A Concise Review. In Neurology India (Vol. 69, Issue 6, pp. 1518–1523). Wolters Kluwer Medknow Publications. https://doi.org/10.4103/0028-3886.333520 Pineda Salazar, D. A., Lopera Restrepo, F., Henao Mag, G. C., Palacio, J. D., & Castellanos, F. X. (2001). Confirmación de la alta prevalencia del trastorno por déficit de atención en una comunidad colombiana. Revista de Neurología, 32(03). https://doi.org/10.33588/rn.3203.2000499 Riaz, A. (2020). Machine Learning for Classification of ADHD. University of London. Riaz, A., Asad, M., Alonso, E., & Slabaugh, G. (2020). DeepFMRI: End-to-end deep learning for functional connectivity and classification of ADHD using fMRI. Journal of Neuroscience Methods, 335. https://doi.org/10.1016/j.jneumeth.2019.108506 Rivas Arribas, L., García Cortázar, P., Grandío Sanjuán, B., Rozados Villaverde, C., Blanco Barca, M.O., & Martínez Reglero, C. (2017). Trastorno por Déficit de Atención e Hiperactividad (TDAH), ¿se mantiene el diagnóstico de sospecha realizado en Atención Primaria en la Unidad de Salud Mental Infanto- Juvenil? Revista de Psiquiatría Infanto-Juvenil, 34(1). https://doi.org/10.31766/revpsij.v34n1a2 Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (Global Edition). In S. Russell & P. Norvig (Eds.), Artificial Intelligence: A Modern Approach (4th ed.). Pearsons Salari, N., Ghasemi, H., Abdoli, N., Rahmani, A., Shiri, M. H., Hashemian, A. H., Akbari, H., & Mohammadi, M. (2023). The global prevalence of ADHD in children and adolescents: a systematic review and meta-analysis. Italian Journal of Pediatrics, 49(1). https://doi.org/10.1186/s13052-023-01456-1 Sargolzaei, S. (2021). Can Deep Learning Hit a Moving Target? A Scoping Review of Its Role to Study Neurological Disorders in Children. Frontiers in Computational Neuroscience, 15. https://doi.org/10.3389/fncom.2021.670489 Sethu, N., & Vyas, R. (2020). Overview of Machine Learning Methods 3 in ADHD Prediction. InAdvances in Bioengineering. Springer Singapore. https://doi.org/10.1007/978-981-15-2063-1 Sibley, M. H., Swanson, J. M., Arnold, L. E., Hechtman, L. T., Owens, E. B., Stehli, A., Abikoff, H., Hinshaw, S. P., Molina, B. S. G., Mitchell, J. T., Jensen, P. S., Howard, A. L., Lakes, K. D., Pelham, W. E., Vitiello, B., Severe, J. B., Eugene Arnold, L., Hoagwood, K., Richters, J., … Stern, K. (2017). Defining ADHD symptom persistence in adulthood: optimizing sensitivity and specificity. Journal of Child Psychology and Psychiatry and Allied Disciplines, 58(6). https://doi.org/10.1111/jcpp.12620 Slater, J., Joober, R., Koborsy, B. L., Mitchell, S., Sahlas, E., & Palmer, C. (2022). Can electroencephalography (EEG) identify ADHD subtypes? A systematic review. In Neuroscience and Biobehavioral Reviews (Vol. 139). https://doi.org/10.1016/j.neubiorev.2022.104752 Swarts, R. (2019). ADHD Screening Tool: Investigating the effectiveness of a tablet-based game with machine learning [Stellenbosch University]. https://scholar.sun.ac.za Ter-Minassian, L., Viani, N., Wickersham, A., Cross, L., Stewart, R., Velupillai, S., & Downs, J. (2022). Assessing machine learning for fair prediction of ADHD in school pupils using a retrospective cohort study of linked education and healthcare data. BMJ Open, 12(12). https://doi.org/10.1136/bmjopen-2021-058058 Ter-Minassian, L., Viani, N., Wickersham, A., Cross, L., Stewart, R., Velupillai, S., & Downs, J. (2023). Assessing machine learning for fair prediction of ADHD in school pupils using a retrospective cohort study of linked education and healthcare data. https://www.repository.cam.ac.uk/handle/1810/346623 Thomas, R., Sanders, S., Doust, J., Beller, E., & Glasziou, P. (2015). Prevalence of attentiondeficit/hyperactivity disorder: A systematic review and meta-analysis. In Pediatrics (Vol. 135, Issue 4). https://doi.org/10.1542/peds.2014-3482 Timimi, S., & Taylor, E. (2004). ADHD is best understood as a cultural construct. In British Journal of Psychiatry (Vol. 184, Issue JAN.). https://doi.org/10.1192/bjp.184.1.8 Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7 Tricco, A. C., Lillie, E., Zarin, W., O’Brien, K. K., Colquhoun, H., Levac, D., Moher, D., Peters, M. D. J., Horsley, T., Weeks, L., Hempel, S., Akl, E. A., Chang, C., McGowan, J., Stewart, L., Hartling, L., Aldcroft, A., Wilson, M. G., Garritty, C., … Straus, S. E. (2018). PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation. In Annals of Internal Medicine (Vol. 169, Issue 7, pp. 467–473). American College of Physicians. https://doi.org/10.7326/M18-0850 Turing, A. M. (2012). Computing machinery and intelligence. In Machine Intelligence: Perspectives on the Computational Model. https://doi.org/10.7551/mitpress/6928.003.0012 U.S. Department of Health & Human Services (HHS). (2025). Adolescent Brain Cognitive Development. HSS. Uyulan, C., Erguzel, T. T., Turk, O., Farhad, S., Metin, B., & Tarhan, N. (2023). A Class Activation Map-Based Interpretable Transfer Learning Model for Automated Detection of ADHD from fMRI Data. Clinical EEG and Neuroscience, 54(2), 151–159. https://doi.org/10.1177/15500594221122699 Wolraich, M. L., Hagan, J. F., Allan, C., Chan, E., Davison, D., Earls, M., Evans, S. W., Flinn, S. K., Froehlich, T., Frost, J., Holbrook, J. R., Lehmann, C. U., Lessin, H. R., Okechukwu, K., Pierce, K. L., Winner, J. D., & Zurhellen, W. (2019a). Clinical practice guideline for the diagnosis, evaluation, and treatment of attention-deficit/hyperactivity disorder in children and adolescents. Pediatrics, 144(4). https://doi.org/10.1542/peds.2019-2528 Wolraich, M. L., Hagan, J. F., Allan, C., Chan, E., Davison, D., Earls, M., Evans, S. W., Flinn, S. K., Froehlich, T., Frost, J., Holbrook, J. R., Lehmann, C. U., Lessin, H. R., Okechukwu, K., Pierce, K. L., Winner, J. D., & Zurhellen, W. (2019b). Clinical Practice Guideline for the Diagnosis, Evaluation, and Treatment of Attention-Deficit/Hyperactivity Disorder in Children and Adolescents. Pediatrics, 144(4). https://doi.org/10.1542/peds.2019-2528 Zakani, Z., Moradi, H., Ghasemzadeh, S., Riazi, M., & Mortazavi, F. (n.d.). The Validity of a Machine Learning-Based Video Game in the Objective Screening of Attention Deficit Hyperactivity Disorder in Children Aged 5 to 12 Years [University of Theran]. https://orcid.org/0009-0005-5622-1843 Zhou, X., Lin, Q., Gui, Y., Wang, Z., Liu, M., & Lu, H. (2021). Multimodal MR Images-Based Diagnosis of Early Adolescent Attention-Deficit/Hyperactivity Disorder Using Multiple Kernel Learning. Frontiers in Neuroscience, 15. https://doi.org/10.3389/fnins.2021.710133 |
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Toledo Arenas, José DanielFranco Zuluaga , ÁlvaroAldas Ávila, Daniela AlejandraBallestero Galeano, Nalfer FranciscoAldas Ávila, Daniela Alejandra [0009-0004-1975-4643]Ballestero Galeano, Nalfer Francisco [0009-0003-9889-4531]2025-07-23T16:38:52Z2025-07-23T16:38:52Z2025-06https://hdl.handle.net/20.500.12495/15042instname:Universidad El Bosquereponame:Repositorio Institucional Universidad El Bosquerepourl:https://repositorio.unbosque.edu.coIntroducción: El trastorno por déficit de atención e hiperactividad (TDAH) presenta una prevalencia del 3-5 % a nivel mundial. Sus manifestaciones clínicas suelen aparecer antes de los 12 años y constituye una de las entidades más frecuentes en la práctica de la psiquiatría infantil. El diagnóstico se basa principalmente en la evaluación clínica, lo que genera elevada variabilidad intersubjetiva y frecuente sub y sobrediagnóstico. Para reducir este problema, se han propuesto diversas pruebas, herramientas y biomarcadores orientados a una valoración más objetiva. Objetivo: Realizar una revisión de alcance sobre la implementación de métodos basados en inteligencia artificial (IA) en el diagnóstico del TDAH. Métodos: Llevamos a cabo una revisión de la literatura indexada y de literatura "gris", en base de datos como Embase, PubMed y Ovid, siguiendo la guía PRISMA para revisiones de alcance (PRISMA-ScR). Llevamos a cabo un análisis descriptivo y crítico de la literatura, enfatizando en las tendencias, lagunas de conocimiento y líneas propuestas de investigación. Resultados: 242 referencias cumplieron los criterios de inclusión. Detallamos un aumento sostenido de la investigación sobre IA aplicada al diagnóstico del TDAH en el periodo estudiado, principalmente en Estados Unidos y China. En la mayoría de los estudios los autores emplearon algoritmos de machine learning (ML) sobre muestras de electroencefalografía (EEG) (35 %) y neuroimágenes (32 %). Las métricas de desempeño de los modelos oscilaron entre el 80 % y el 100 % para diferenciar casos de controles. Pese a su potencial, evidenciamos limitaciones metodológicas y prácticas que restringen su adopción generalizada de la aplicación de la IA en el diagnóstico del TDAH. Conclusiones: En la actualidad, la IA no sustituye la entrevista clínica, el análisis multidimensional ni la evaluación contextual realizada por especialistas humanos; sigue siendo una herramienta complementaria al juicio clínico.Especialista en Psiquiatría Infantil y del AdolescenteEspecializaciónIntroduction: Attention Deficit Hyperactivity Disorder (ADHD) has a prevalence of 5-10% worldwide. Its clinical manifestations usually appear before the age of 12 and constitute one of the most common entities in the practice of child psychiatry. The diagnosis is mainly based on clinical evaluation, which generates high intersubjective variability and frequent under- and overdiagnosis. To reduce this problem, various tests, tools, and biomarkers aimed at a more objective assessment have been proposed. Objective: To conduct a scoping review on the implementation of artificial intelligence (AI)-based methods in the diagnosis of ADHD. Methods: We conducted a review of indexed literature and "grey" literature, using databases such as Embase, PubMed, and Ovid, following the PRISMA guidelines for scoping reviews (PRISMA-ScR). We conducted a descriptive and critical analysis of the literature, emphasizing trends, knowledge gaps, and proposed lines of research. Results: 242 references met the inclusion criteria. We detail a sustained increase in research on AI applied to ADHD diagnosis during the studied period, mainly in the United States and China. In most studies, the authors employed machine learning (ML) algorithms on electroencephalography (EEG) samples (35%) and neuroimaging (32%). The performance metrics of the models ranged between 80% and 100% for differentiating cases from controls. Despite its potential, we observed methodological and practical limitations that restrict its widespread adoption of AI application in ADHD diagnosis. Conclusions: Currently, AI does not replace the clinical interview, multidimensional analysis, or contextual evaluation conducted by human specialists; it remains a complementary tool to clinical judgment.application/pdfAttribution-NonCommercial-ShareAlike 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-sa/4.0/Acceso abiertohttps://purl.org/coar/access_right/c_abf2http://purl.org/coar/access_right/c_abf2Revisión de alcanceDiagnósticoDéficit de Atención e HiperactividadNiñezInteligencia ArtificialAprendizaje AutomaticoScoping ReviewDiagnosisAttention deficit hyperactivity disorder (ADHD)ChildArtificial intelligence (AI)Machine learningWS 350Scoping Review sobre el uso de la Inteligencia Artificial (AI) en el diagnóstico del Déficit de Atención e Hiperactividad (TDAH) en menores de 18 años, durante los años 2019 a 2024Scoping Review on the Use of Artificial Intelligence (AI) in the Diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) in Individuals Under 18 Years of Age, 2019–2024Especialización en Psiquiatría Infantil y del AdolescenteUniversidad El BosqueFacultad de MedicinaTesis/Trabajo de grado - Monografía - Especializaciónhttps://purl.org/coar/resource_type/c_7a1fhttp://purl.org/coar/resource_type/c_7a1finfo:eu-repo/semantics/bachelorThesishttps://purl.org/coar/version/c_ab4af688f83e57aaAhire, N., Awale, R. N., & Wagh, A. (2023). Comprehensive review of EEG data classification techniques for ADHD detection using machine learning and deep learning. In Romanian Journal of Pediatrics (Vol. 72, Issue 2, pp. 57–66). Amaltea Medical Publishing House. https://doi.org/10.37897/RJP.2023.2.1American Psychiatric Association. (2013). DSM-5. In Madrid (5th ed.). Editorial Médica Panamericana. https://doi.org/10.1176/appi.books.9780890425596.744053Amigo Martins, L., & Romina Leardi, M. (2025). Attention Deficit Hyperactivity Disorder: Late diagnosis and its psychosocial and functional consequences. South Health and Policy, 4, 206. https://doi.org/10.56294/shp2025206Artificial Intelligence/Machine Learning-enabled Working Group. (2024). Good machine learning practice for medical device development: Guiding principles AUTHORING GROUP Artificial Intelligence/Machine Learning-enabled Working Group Preface.Berrezueta-Guzman, J., Krusche, S., Serpa-Andrade, L., & Martín-Ruiz, M. L. (2023). Artificial Vision Algorithm for Behavior Recognition in Children with ADHD in a Smart Home Environment. Lecture Notes in Networks and Systems, 542 LNNS, 661–671. https://doi.org/10.1007/978-3-031-16072-1_47Biederman, J., Faraone, S. V., Spencer, T. J., Mick, E., Monuteaux, M. C., & Aleardi, M. (2006). Functional impairments in adults with self-reports of diagnosed ADHD: A controlled study of 1001 adults in the community. In Journal of Clinical Psychiatry (Vol. 67, Issue 4). https://doi.org/10.4088/JCP.v67n0403Celis, M. de. (2014). Volviendo a la Normalidad. La Invención del TDAH y del Trastorno Bipolar Infantil. Clínica Contemporánea, 5(3). https://doi.org/10.5093/cc2014a22Chang, L. Y., Wang, M. Y., & Tsai, P. S. (2016). Diagnostic accuracy of Rating Scales for attentiondeficit/hyperactivity disorder: A meta-analysis. Pediatrics, 137(3). https://doi.org/10.1542/peds.2015-2749Chen, C. C., Wu, E. H. K., Chen, Y. Q., Tsai, H. J., Chung, C. R., & Yeh, S. C. (2023). Neuronal Correlates of Task Irrelevant Distractions Enhance the Detection of Attention Deficit/Hyperactivity Disorder. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, 1302–1310. https://doi.org/10.1109/TNSRE.2023.3241649Chen, H., Chen, W., Song, Y., Sun, L., & Li, X. (2019). EEG characteristics of children with attentiondeficit/hyperactivity disorder. Neuroscience, 406, 444–456. https://doi.org/10.1016/j.neuroscience.2019.03.048Chen, H., Yang, Y., Odisho, D., Wu, S., Yi, C., & Oliver, B. G. (2023). Can biomarkers be used to diagnose attention deficit hyperactivity disorder? Frontiers in Psychiatry, 14. https://doi.org/10.3389/FPSYT.2023.1026616/FULLChen, T., Tachmazidis, I., Batsakis, S., Adamou, M., Papadakis, E., & Antoniou, G. (2023). Diagnosing attention-deficit hyperactivity disorder (ADHD) using artificial intelligence: a clinical study in the UK. Frontiers in Psychiatry, 14. https://doi.org/10.3389/fpsyt.2023.1164433CIE-11. (2023). Clasificación Internacional de Enfermedades, 11.a revisión (CIE 11). Oms.Cornejo Ochoa, J. W., Osío Uribe, Ó., Sánchez Mosquera, Y., Carrizosa Moog, J., Sánchez Aldana, G., Grisales Romero, H., Castillo Parra, H., & Holguín Acosta, J. (2005). Prevalencia del trastorno por déficit de atención-hiperactividad en niños y adolescentes colombianos. Revista de Neurología, 40(12), 716. https://doi.org/10.33588/rn.4012.2004569Crichton A. (1978). An inquiry into the nature and origin of mental derangement: comprehending a concise system of the physiology and pathology of the human mind and a history of the passions and their effects.De Lacy, N., & Ramshaw, M. J. (2023). Selectively predicting the onset of ADHD, oppositional defiant disorder, and conduct disorder in early adolescence with high accuracy. Frontiers in Psychiatry, 14. https://doi.org/10.3389/fpsyt.2023.1280326De Souza, I., Mattos, P., Pina, C., & Fortes, D. (2008). ADHD: The impact when not diagnosed. Jornal Brasileiro de Psiquiatria, 57(2). https://doi.org/10.1590/s0047-20852008000200010Duda, M., Ma, R., Haber, N., & Wall, D. P. (2016). Use of machine learning for behavioral distinction of autism and ADHD. Translational Psychiatry, 6(2). https://doi.org/10.1038/tp.2015.221Eslami, T., Almuqhim, F., Raiker, J. S., & Saeed, F. (2021). Machine Learning Methods for Diagnosing Autism Spectrum Disorder and Attention- Deficit/Hyperactivity Disorder Using Functional and Structural MRI: A Survey. In Frontiers in Neuroinformatics (Vol. 14). Frontiers Media S.A. https://doi.org/10.3389/fninf.2020.575999Espinet, S. D., Graziosi, G., Toplak, M. E., Hesson, J., & Minhas, P. (2022). A Review of Canadian Diagnosed ADHD Prevalence and Incidence Estimates Published in the Past Decade. In Brain Sciences (Vol. 12, Issue 8). https://doi.org/10.3390/brainsci12081051Faraone, S. V., Biederman, J., & Mick, E. (2006). The age-dependent decline of attention deficit hyperactivity disorder: A meta-analysis of follow-up studies. In Psychological Medicine (Vol. 36,Issue 2). https://doi.org/10.1017/S003329170500471XFaraone, S. V., Bonvicini, C., & Scassellati, C. (2014). Biomarkers in the Diagnosis of ADHD – Promising Directions. In Current Psychiatry Reports (Vol. 16, Issue 11). https://doi.org/10.1007/s11920-014-0497-1Faraone, S. V., & Larsson, H. (2019). Genetics of attention deficit hyperactivity disorder. In Molecular Psychiatry (Vol. 24, Issue 4). https://doi.org/10.1038/s41380-018-0070-0Flores, M. G. (2023). Industry perspective of artificial intelligence in medicine and surgery. In Intelligence-Based Cardiology and Cardiac Surgery: Artificial Intelligence and Human Cognition in Cardiovascular Medicine. https://doi.org/10.1016/B978-0-323-90534-3.00031-7Furlong, S., Cohen, J. R., Hopfinger, J., Snyder, J., Robertson, M. M., & Sheridan, M. A. (2021). Resting-state EEG Connectivity in Young Children with ADHD. Journal of Clinical Child and Adolescent Psychology, 50(6), 746–762. https://doi.org/10.1080/15374416.2020.1796680Geneau, M. (2022a). L’intelligence artificielle au service de la santé mentale.Geneau, M. (2022b). L’intelligence artificielle au service de la santé mentale. Université LAVAL.Giacobini, M. B., Ahnemark, E., Medin, E., Freilich, J., Andersson, M., Ma, Y., & Ginsberg, Y. (2023). Epidemiology, Treatment Patterns, Comorbidities, and Concomitant Medication in Patients with ADHD in Sweden: A Registry-Based Study (2018–2021). Journal of Attention Disorders, 27(12). https://doi.org/10.1177/10870547231177221Good machine learning practice for medical device development: Guiding principles AUTHORING GROUP Artificial Intelligence/Machine Learning-enabled Working Group Preface. (2023)Gualtieri, C. T., & Johnson, L. G. (2005a). ADHD: Is Objective Diagnosis Possible? Psychiatry (Edgmont (Pa. : Township)), 2(11).Gualtieri, C. T., & Johnson, L. G. (2005b). ADHD: Is Objective Diagnosis Possible? Psychiatry (Edgmont (Pa. : Township)), 2(11).He, Y., Wang, X., Yang, Z., Xue, L., Chen, Y., Ji, J., Wan, F., Mukhopadhyay, S. C., Men, L., Tong, M. C. F., Li, G., & Chen, S. (2023). Classification of attention deficit/hyperactivity disorder based on EEG signals using a EEG-Transformer model. Journal of Neural Engineering, 20(5). https://doi.org/10.1088/1741-2552/acf7f5Hintze, A. (2016, November 18). Understanding the Four Types of AI, From Reactive Robots to Self-Aware Beings. Observer.Hoogman, M., Bralten, J., Hibar, D. P., Mennes, M., Zwiers, M. P., Schweren, L. S. J., van Hulzen, K.J. E., Medland, S. E., Shumskaya, E., Jahanshad, N., Zeeuw, P. de, Szekely, E., Sudre, G., Wolfers, T., Onnink, A. M. H., Dammers, J. T., Mostert, J. C., Vives-Gilabert, Y., Kohls, G., … Franke, B. (2017). Subcortical brain volume differences of participants with ADHD across the lifespan: an ENIGMA collaboration. The Lancet. Psychiatry, 4(4).IBM. (n.d.). ¿Qué es la inteligencia artificial (IA)? Retrieved April 9, 2024, from https://www.ibm.com/mx-es/topics/artificial-intelligenceKautzky, A., Vanicek, T., Philippe, C., Kranz, G. S., Wadsak, W., Mitterhauser, M., Hartmann, A., Hahn, A., Hacker, M., Rujescu, D., Kasper, S., & Lanzenberger, R. (2020). Machine learning classification of ADHD and HC by multimodal serotonergic data. Translational Psychiatry, 10(1). https://doi.org/10.1038/s41398-020-0781-2Kazda, L., Bell, K., Thomas, R., McGeechan, K., Sims, R., & Barratt, A. (2021). Overdiagnosis of Attention-Deficit/Hyperactivity Disorder in Children and Adolescents. JAMA Network Open, 4(4),e215335. https://doi.org/10.1001/jamanetworkopen.2021.5335Kulkarni, V., Nemade, B., Patel, S., Patel, K., & Velpula, S. (2024). A short report on ADHD detection using convolutional neural networks. Frontiers in Psychiatry, 15. https://doi.org/10.3389/fpsyt.2024.1426155Kuntsi, J., Larsson, H., Deng, Q., Lichtenstein, P., & Chang, Z. (2022). The Combined Effects of Young Relative Age and Attention-Deficit/Hyperactivity Disorder on Negative Long-term Outcomes. Journal of the American Academy of Child and Adolescent Psychiatry, 61(2). https://doi.org/10.1016/j.jaac.2021.07.002Latifi, B., Amini, A., & Motie Nasrabadi, A. (2024). Siamese based deep neural network for ADHD detection using EEG signal. Computers in Biology and Medicine, 182. https://doi.org/10.1016/j.compbiomed.2024.109092Lee, D. W., Lee, S. H., Ahn, D. H., Lee, G. H., Jun, K., & Kim, M. S. (2023). Development of a Multiple RGB-D Sensor System for ADHD Screening and Improvement of Classification Performance Using Feature Selection Method. Applied Sciences (Switzerland), 13(5). https://doi.org/10.3390/app13052798Lin, H., Haider, S. P., Kaltenhauser, S., Mozayan, A., Malhotra, A., Constable, R. T., Scheinost, D., Ment, L. R., Konrad, K., & Payabvash, S. (2023). Population level multimodal neuroimaging correlates of attention-deficit hyperactivity disorder among children. Frontiers in Neuroscience, 17. https://doi.org/10.3389/fnins.2023.1138670Liu, Z., Li, J., Zhang, Y., Wu, D., Huo, Y., Yang, J., Zhang, M., Dong, C., Jiang, L., Sun, R., Zhou, R., Li, F., Yu, X., Zhu, D., Guo, Y., & Chen, J. (2024). Auxiliary Diagnosis of Children With Attention-Deficit/Hyperactivity Disorder Using Eye Tracking and Digital Biomarkers: Case-Control Study. JMIR Mhealth and Uhealth, 12, e58927. https://doi.org/10.2196/58927Lucía Caselles-Pina, Alejandro Quesada-López, Eva María Garzón Hernández, David Delgado-Gómez, & Aaron, S. (2023). A systematic review on the application of machine learning models in psychometric questionnaires for the diagnosis of attention deficit hyperactivity disorder. European Journal of Neuroscience, 60(62), 4115–4127. https://doi.org/10.13039/501100011033Lynch, S., McDonnell, T., Leahy, D., Gavin, B., & McNicholas, F. (2023). Prevalence of mental health disorders in children and adolescents in the Republic of Ireland: A systematic review. In Irish Journal of Psychological Medicine (Vol. 40, Issue 1). https://doi.org/10.1017/ipm.2022.46Manjur, S. M., Diaz, L. R. M., Lee, I. O., Skuse, D. H., Thompson, D. A., Marmolejos-Ramos, F., Constable, P. A., & Posada-Quintero, H. F. (2024). Detecting Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder Using Multimodal Time-Frequency Analysis with Machine Learning Using the Electroretinogram from Two Flash Strengths. Journal of Autism and Developmental Disorders. https://doi.org/10.1007/s10803-024-06290-wMcCarthy, J. (1998). WHAT IS ARTIFICIAL INTELLIGENCE. https://api.semanticscholar.org/CorpusID:260698276Miodovnik, A., Harstad, E., Sideridis, G., & Huntington, N. (2015). Timing of the diagnosis of attention-deficit/hyperactivity disorder and autism spectrum disorder. Pediatrics, 136(4). https://doi.org/10.1542/peds.2015-1502Misgar, M. M., & Bhatia, M. P. S. (2024). Advancing ADHD diagnosis: using machine learning for unveiling ADHD patterns through dimensionality reduction on IoMT actigraphy signals. International Journal of Information Technology (Singapore). https://doi.org/10.1007/s41870-024-01895-xNational Institute for Health and Care Excellence. (2008). Attention deficit hyperactivity activity disorder : diagnosis and management. NICE Guideline, SeptemberNational Institute for Health and Care Excellence. (2024). Digital technologies for assessing attention deficit hyperactivity disorder (ADHD) Diagnostics guidance. www.nice.org.uk/guidance/dg60National Resource Center on ADHD. (2017). aboutADHD.Neuroimaging Informatics Tools and Resources Clearinghouse. (2011). La muestra del ADHD-200.O’Mahony, N., Florentino-Liano, B., Carballo, J. J., Baca-García, E., & Rodríguez, A. A. (2014). Objective diagnosis of ADHD using IMUs. Medical Engineering and Physics, 36(7). https://doi.org/10.1016/j.medengphy.2014.02.023Organización Mundial de la salud. (2021). Ethics and governance of artificial intelligence for health: WHO guidance. OMS, 1.Palacios-Cruz, L., de la Peña, F., Valderrama, A., Patiño, R., Portugal, S. P. C., & Ulloa, R. E. (2011). Conocimientos, creencias y actitudes en padres mexicanos acerca del trastorno por déficit de atención con hiperactividad (TDAH). Salud Mental, 34(2).Pereira-Sanchez, V., & Castellanos, F. X. (2021). Neuroimaging in attention-deficit/hyperactivity disorder. In Current Opinion in Psychiatry (Vol. 34, Issue 2). https://doi.org/10.1097/YCO.0000000000000669Periyasamy, R., Vibashan, V., Varghese, G., & Aleem, M. (2021a). Machine Learning Techniques for the Diagnosis of Attention-Deficit/Hyperactivity Disorder from Magnetic Resonance Imaging: A Concise Review. In Neurology India (Vol. 69, Issue 6). https://doi.org/10.4103/0028-3886.333520Periyasamy, R., Vibashan, V., Varghese, G., & Aleem, M. (2021b). Machine Learning Techniques for the Diagnosis of Attention-Deficit/Hyperactivity Disorder from Magnetic Resonance Imaging: A Concise Review. In Neurology India (Vol. 69, Issue 6, pp. 1518–1523). Wolters Kluwer Medknow Publications. https://doi.org/10.4103/0028-3886.333520Periyasamy, R., Vibashan, V., Varghese, G., & Aleem, M. (2021c). Machine Learning Techniques for the Diagnosis of Attention-Deficit/Hyperactivity Disorder from Magnetic Resonance Imaging: A Concise Review. In Neurology India (Vol. 69, Issue 6, pp. 1518–1523). Wolters Kluwer Medknow Publications. https://doi.org/10.4103/0028-3886.333520Pineda Salazar, D. A., Lopera Restrepo, F., Henao Mag, G. C., Palacio, J. D., & Castellanos, F. X. (2001). Confirmación de la alta prevalencia del trastorno por déficit de atención en una comunidad colombiana. Revista de Neurología, 32(03). https://doi.org/10.33588/rn.3203.2000499Riaz, A. (2020). Machine Learning for Classification of ADHD. University of London.Riaz, A., Asad, M., Alonso, E., & Slabaugh, G. (2020). DeepFMRI: End-to-end deep learning for functional connectivity and classification of ADHD using fMRI. Journal of Neuroscience Methods, 335. https://doi.org/10.1016/j.jneumeth.2019.108506Rivas Arribas, L., García Cortázar, P., Grandío Sanjuán, B., Rozados Villaverde, C., Blanco Barca, M.O., & Martínez Reglero, C. (2017). Trastorno por Déficit de Atención e Hiperactividad (TDAH), ¿se mantiene el diagnóstico de sospecha realizado en Atención Primaria en la Unidad de Salud Mental Infanto- Juvenil? Revista de Psiquiatría Infanto-Juvenil, 34(1). https://doi.org/10.31766/revpsij.v34n1a2Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (Global Edition). In S. Russell & P. Norvig (Eds.), Artificial Intelligence: A Modern Approach (4th ed.). PearsonsSalari, N., Ghasemi, H., Abdoli, N., Rahmani, A., Shiri, M. H., Hashemian, A. H., Akbari, H., & Mohammadi, M. (2023). The global prevalence of ADHD in children and adolescents: a systematic review and meta-analysis. Italian Journal of Pediatrics, 49(1). https://doi.org/10.1186/s13052-023-01456-1Sargolzaei, S. (2021). Can Deep Learning Hit a Moving Target? A Scoping Review of Its Role to Study Neurological Disorders in Children. Frontiers in Computational Neuroscience, 15. https://doi.org/10.3389/fncom.2021.670489Sethu, N., & Vyas, R. (2020). Overview of Machine Learning Methods 3 in ADHD Prediction. InAdvances in Bioengineering. Springer Singapore. https://doi.org/10.1007/978-981-15-2063-1Sibley, M. H., Swanson, J. M., Arnold, L. E., Hechtman, L. T., Owens, E. B., Stehli, A., Abikoff, H., Hinshaw, S. P., Molina, B. S. G., Mitchell, J. T., Jensen, P. S., Howard, A. L., Lakes, K. D., Pelham, W. E., Vitiello, B., Severe, J. B., Eugene Arnold, L., Hoagwood, K., Richters, J., … Stern, K. (2017). Defining ADHD symptom persistence in adulthood: optimizing sensitivity and specificity. Journal of Child Psychology and Psychiatry and Allied Disciplines, 58(6). https://doi.org/10.1111/jcpp.12620Slater, J., Joober, R., Koborsy, B. L., Mitchell, S., Sahlas, E., & Palmer, C. (2022). Can electroencephalography (EEG) identify ADHD subtypes? A systematic review. In Neuroscience and Biobehavioral Reviews (Vol. 139). https://doi.org/10.1016/j.neubiorev.2022.104752Swarts, R. (2019). ADHD Screening Tool: Investigating the effectiveness of a tablet-based game with machine learning [Stellenbosch University]. https://scholar.sun.ac.zaTer-Minassian, L., Viani, N., Wickersham, A., Cross, L., Stewart, R., Velupillai, S., & Downs, J. (2022). Assessing machine learning for fair prediction of ADHD in school pupils using a retrospective cohort study of linked education and healthcare data. BMJ Open, 12(12). https://doi.org/10.1136/bmjopen-2021-058058Ter-Minassian, L., Viani, N., Wickersham, A., Cross, L., Stewart, R., Velupillai, S., & Downs, J. (2023). Assessing machine learning for fair prediction of ADHD in school pupils using a retrospective cohort study of linked education and healthcare data. https://www.repository.cam.ac.uk/handle/1810/346623Thomas, R., Sanders, S., Doust, J., Beller, E., & Glasziou, P. (2015). Prevalence of attentiondeficit/hyperactivity disorder: A systematic review and meta-analysis. In Pediatrics (Vol. 135, Issue 4). https://doi.org/10.1542/peds.2014-3482Timimi, S., & Taylor, E. (2004). ADHD is best understood as a cultural construct. In British Journal of Psychiatry (Vol. 184, Issue JAN.). https://doi.org/10.1192/bjp.184.1.8Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7Tricco, A. C., Lillie, E., Zarin, W., O’Brien, K. K., Colquhoun, H., Levac, D., Moher, D., Peters, M. D. J., Horsley, T., Weeks, L., Hempel, S., Akl, E. A., Chang, C., McGowan, J., Stewart, L., Hartling, L., Aldcroft, A., Wilson, M. G., Garritty, C., … Straus, S. E. (2018). PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation. In Annals of Internal Medicine (Vol. 169, Issue 7, pp. 467–473). American College of Physicians. https://doi.org/10.7326/M18-0850Turing, A. M. (2012). Computing machinery and intelligence. In Machine Intelligence: Perspectives on the Computational Model. https://doi.org/10.7551/mitpress/6928.003.0012 U.S. Department of Health & Human Services (HHS). (2025). Adolescent Brain Cognitive Development. HSS.Uyulan, C., Erguzel, T. T., Turk, O., Farhad, S., Metin, B., & Tarhan, N. (2023). A Class Activation Map-Based Interpretable Transfer Learning Model for Automated Detection of ADHD from fMRI Data. Clinical EEG and Neuroscience, 54(2), 151–159. https://doi.org/10.1177/15500594221122699Wolraich, M. L., Hagan, J. F., Allan, C., Chan, E., Davison, D., Earls, M., Evans, S. W., Flinn, S. K., Froehlich, T., Frost, J., Holbrook, J. R., Lehmann, C. U., Lessin, H. R., Okechukwu, K., Pierce, K. L., Winner, J. D., & Zurhellen, W. (2019a). Clinical practice guideline for the diagnosis, evaluation, and treatment of attention-deficit/hyperactivity disorder in children and adolescents. Pediatrics, 144(4). https://doi.org/10.1542/peds.2019-2528Wolraich, M. L., Hagan, J. F., Allan, C., Chan, E., Davison, D., Earls, M., Evans, S. W., Flinn, S. K., Froehlich, T., Frost, J., Holbrook, J. R., Lehmann, C. U., Lessin, H. R., Okechukwu, K., Pierce, K. L., Winner, J. D., & Zurhellen, W. (2019b). Clinical Practice Guideline for the Diagnosis, Evaluation, and Treatment of Attention-Deficit/Hyperactivity Disorder in Children and Adolescents. Pediatrics, 144(4). https://doi.org/10.1542/peds.2019-2528Zakani, Z., Moradi, H., Ghasemzadeh, S., Riazi, M., & Mortazavi, F. (n.d.). The Validity of a Machine Learning-Based Video Game in the Objective Screening of Attention Deficit Hyperactivity Disorder in Children Aged 5 to 12 Years [University of Theran]. https://orcid.org/0009-0005-5622-1843Zhou, X., Lin, Q., Gui, Y., Wang, Z., Liu, M., & Lu, H. (2021). Multimodal MR Images-Based Diagnosis of Early Adolescent Attention-Deficit/Hyperactivity Disorder Using Multiple Kernel Learning. 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