Diagnóstico de TDAH con Machine Learning y Sensores: Un Mapeo Sistemático
DOI:
https://doi.org/10.32870/recibe.v12i2.331Palabras clave:
TDAH, Aprendizaje Maquina, Diagnostico, Trastorno por déficit de atención con hiperactividad, SensoresResumen
El trastorno de déficit de atención con hiperactividad (TDAH) es un trastorno del neurodesarrollo que tiene como características principales la hiperactividad y la falta concentración en actividades cotidianas. Es un trastorno conocido por darse a tempranas edades y afectar a el desempeño escolar. El diagnóstico del TDAH ha sido un procedimiento complejo, al necesitar una gran recolección y análisis de datos de manera manual. Por esto mismo, se ha propuesto el uso de las herramientas de machine learning (ML) para mejorar la precisión y el tiempo del diagnóstico. Actualmente, existen una proliferación de las metodologías que reconocen diferentes tipos de datos de pacientes para el diagnóstico del TDAH. El objetivo de este estudio es revisar dentro del estado del arte las investigaciones previas sobre está área y responder los cuestionamientos planteados. En el artículo se discutirán temas sobre los modelos de machine learning y los diferentes datos recolectados. Entre los resultados se ve como modelos de redes neuronales y Support Vector Machine (SVM) son los de mayor frecuencia. Además, que los datos de movimiento y aceleración tienen un mayo desempeño a comparación de las implementaciones por procesos neuronales a contra posición de lo esperado. Podemos concluir que los estudios relacionados a la adquisición de datos de movimiento tienen una gran promesa en su implementación y desarrollo en tiempos futuros para una mayor precisión en los resultados clínicos.Citas
Johnson, M. H. (2005). Developmental cognitive neuroscience: An introduction. Wiley-Blackwell.
National Institute of Mental Health. (2021). Attention-Deficit/Hyperactivity Disorder (ADHD). Retrieved from https://www.nimh.nih.gov/health/topics/attention-deficit-hyperactivity-disorder- adhd/index.shtml
Polanczyk G. V., Willcutt E. G., et. al. (2014). ADHD prevalence estimates across three decades: An updated systematic review and meta-regression analysis. International Journal of Epidemiology, 43(2), 434-442.
American Academy of Pediatrics. (2019). Clinical practice guideline for the diagnosis, evaluation, and treatment of attention-deficit/hyperactivity disorder in children and adolescents. Pediatrics, 144(4), e20192528.
Rader, R., McCauley, L., and Callen, E. C. (2009). Current strategies in the diagnosis and treatment of childhood attention-deficit/hyperactivity disorder. American Family Physician, 79(8), 657-665.
Kemper AR, Maslow GR, et al. (2018). Attention Deficit Hyperactivity Disorder: Diagnosis and Treatment in Children and Adolescents. Agency for Healthcare Research and Quality (US). Comparative Effectiveness Reviews, No. 203
Muñoz-Organero, M., Powell, L., Heller, B., Harpin, V., and Parker, J. (2018). Automatic Extraction and Detection of Characteristic Movement Patterns in Children with ADHD Based on a Convolutional Neural Network (CNN) and Acceleration Images. Sensors (Basel). 18(11): 3924.
Slobodin, O., Yahav, I., Berger, I. (2020). A Machine-Based Prediction Model of ADHD Using CPT Data. Front Hum Neurosci. 14:560021. eCollection.
Periyasamy, R., Vibashan, V., Varghese, G., Aleem, M., (2021). Machine Learning Techniques for the Diagnosis of Attention-Deficit/Hyperactivity Disorder from Magnetic Resonance Imaging: A Concise Review. 69(6):1518-1523.
Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele University, Technical Report TR/SE-0401.
Sáenz, A., Villemonteix, T., Massat, I., (2018). Structural and functional neuroimaging in attention-deficit/hyperactivity disorder. Dev Med Child Neurol 61(4):399-405. doi: 10.1111/dmcn.14050.
Carr, L., Henderson, J., Nigg, J., (2010). Cognitive Control and Attentional Selection in Adolescents with ADHD Versus ADD. Pages 726-740.
Lee W., Lee D., et. al. (2023). Deep-Learning-Based ADHD Classification Using Children’s Skeleton Data Acquired through the ADHD Screening Game, 23(1), 246
Lindhiem O., Goel M., et. al. (2022) Objective Measurement of Hyperactivity Using Mobile Sensing and Machine Learning: Pilot Study
Hamedi N., Khadem A., et. al.(2022) Detecting ADHD Based on Brain Functional Connectivity Using Resting-State MEG Signals, Vol.9, No.2 110-118.
Kim S., Baek J., et. al.(2021) Machine-learning-based diagnosis of drug-naive adult patients with attention-deficit hyperactivity disorder using mismatch negativity, 11:484 ;
Sanchez A., Villanueva C., et. al. (2021) Classification of brain signals for RPAS control in the treatment of attention deficit hyperactivity disorder. vol.97 no.2.
N. Hamedi, A. Khadem, et. al. (2021). An Effective Connectomics Approach for Diagnosing ADHD using Eyes-open Resting-state MEG, "11th International Conference on Computer Engineering and Knowledge (ICCKE), 110-114,
Chen H., Song Y., Li X. (2019). A deep learning framework for identifying children with ADHD using an EEG-based brain network. Neurocomputing, 356, 83-96.
Chen H., Chen W., et. al. (2019). EEG characteristics of children with attention-deficit/hyperactivity disorder. Neuroscience, 406, 444-456.
Jaiswal S., Valstar M. F., et. al. (2017). Automatic Detection of ADHD and ASD from Expressive Behaviour in RGBD Data. 12th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2017), 762-769,
Faedda G. L., Ohashi K., et. al. (2016). Actigraph measures discriminate pediatric bipolar disorder from attention-deficit/hyperactivity disorder and typically developing controls. Journal of Child Psychology and Psychiatry, 57(6), 706-716.
O’Mahony N., Florentino-Liano B., et. al. (2014). Objective diagnosis of ADHD using IMUs. Medical Engineering and Physics, 36(7), 922-926.
Dolón-Poza, M., Berrezueta-Guzman, J., Martín-Ruiz, M., (2020). Creation of an Intelligent System to Support the Therapy Process in Children with ADHD.: TICEC 2020, CCIS 1307, pp. 36–50, 2020.
Gu, Y., Miao, S., et. al., (2022). ADHD Children Identification with Multiview Feature Fusion of fNIRS Signals. IEEE Sensors Journal, vol. 22, NO. 13,
Deok-Won Lee, Sang-hyub Lee, et. al. (2023) Development of a Multiple RGB-D Sensor System for ADHD Screening and Improvement of Classification Performance Using Feature Selection Method. Sci. 2023, 13, 2798.
Dongwei Li, Xiangsheng Luo, et. al. (2022). Yan Song Information-based multivariate decoding reveals imprecise neural encoding in children with attention deficit hyperactivity disorder during visual selective attention. 44:937–947
Shengbing Pei, Chaoqun Wang, Shuai Cao, Member, IEEE, and Zhao Lv (2023). Data Augmentation for fMRI-Based Functional Connectivity and its Application to Cross-Site ADHD Classification. ieee transactions on instrumentation and measurement, vol. 72
Xin Qin, Jindong, Wang, Yiqiang Chen, et. al. (2022). Domain Generalization for Activity Recognition via Adaptive Feature Fusion. 2157-6904
Xiaocheng Zhou, Qingmin Lin, et. al. (2021). Multimodal MR Images-Based Diagnosis of Early Adolescent Attention-Deficit/Hyperactivity Disorder Using Multiple Kernel Learning. Front. Neurosci. 15:710133.
Ming Chen, BS, Hailong Li, et. al. (2019). A Multichannel Deep Neural Network Model Analyzing Multiscale Functional Brain Connectome Data for Attention Deficit Hyperactivity Disorder Detection. 2(1):e190012
Amado-Caballero, P., Casaseca-de-la-Higuera, P., et. al. (2023). Insight into ADHD diagnosis with deep learning on Actimetry: Quantitative interpretation of occlusion maps in age and gender subgroups. Artificial Intelligence In Medicine 143 (2023) 102630.
Wen Loh, H., Ping Ooi, C., et. al. (2022) Automated detection of ADHD: Current trends and future perspective. Computers in Biology and Medicine 146 (2022) 105525.
Zhi-Hua Zhou (2021) Machine Learning. Springer Nature, 2021
