Aplicações de Machine Learning na Detecção e Monitoramento de Distúrbios de Neurodesenvolvimento
Resumo
Este artigo apresenta um Mapeamento Sistemático da Literatura (MSL) sobre o uso de ML na detecção e monitoramento de Transtorno do Espectro Autista (TEA), Transtorno de Déficit de Atenção e Hiperatividade (TDAH) e Dislexia. O objetivo é investigar e analisar a aplicação de algoritmos de ML para diagnóstico, monitoramento e suporte desses distúrbios. Seguindo o protocolo PRISMA, a busca na IEEE Xplore resultou em 82 artigos, com 50 incluídos. Os achados indicam um uso crescente e promissor de ML, avaliando sua eficácia em relação aos métodos tradicionais.Referências
Al-Qazzaz, N. K., Aldoori, A. A., Buniya, A. K., Ali, S. H. B. M., and Ahmad, S. A. (2024). Transfer learning and hybrid deep convolutional neural networks models for autism spectrum disorder classification from eeg signals. IEEE Access, 12:64510–64530.
Briguglio, M., Turriziani, L., Currò, A., Gagliano, A., Di Rosa, G., Caccamo, D., Tonacci, A., and Gangemi, S. (2023). A machine learning approach to the diagnosis of autism spectrum disorder and multi-systemic developmental disorder based on retrospective data and ados-2 score. Brain Sciences.
Fombonne, E. (2003). Epidemiological surveys of autism and other pervasive developmental disorders: An update. Journal of Autism and Developmental Disorders, 33(4):365–382.
Han, J., Jiang, G., Ouyang, G., and Li, X. (2022). A multimodal approach for identifying autism spectrum disorders in children. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 30:2003–2011.
Hasan, S. M. M., Uddin, M. P., Mamun, M. A., Sharif, M. I., Ulhaq, A., and Krishnamoorthy, G. (2023). A machine learning framework for early-stage detection of autism spectrum disorders. IEEE Access, 11:2168–21785.
Heinsfeld, A. S., Franco, A. R., Craddock, R. C., Buchweitz, A., and Meneguzzi, F. (2017). Identification of autism spectrum disorder using deep learning and the abide dataset. NeuroImage: Clinical.
Henderson, B., Yogarajah, P., Gardiner, B., and McGinnity, T. M. (2023). Encoding kinematic and temporal gait data in an appearance-based feature for the automatic classification of autism spectrum disorder. IEEE Access, 11:134100–134117.
Kavadi, D. P., Chirra, V. R. R., Kumar, P. R., Veesam, S. B., Yeruva, S., and Pappala, L. K. (2024). A hybrid machine learning model for accurate autism diagnosis. IEEE Access, 12:194911–194921.
Kim, B., Park, J., Kim, T., and Kwon, Y. (2023). Finding essential parts of the brain in rs-fmri can improve adhd diagnosis using deep learning. IEEE Access, 11:116065–116075.
Liang, Y., Liu, B., and Zhang, H. (2021). A convolutional neural network combined with prototype learning framework for brain functional network classification of autism spectrum disorder. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 29:2193–2202.
Martins, F. (2022). Entre 5% e 8% da população mundial apresenta transtorno de déficit de atenção com hiperatividade. [link]. Acesso em: 15 abr. 2025.
Moher, D., Liberati, A., Tetzlaff, J., and Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: The prisma statement. PLoS Medicine.
Robles, M., Namdarian, N., Otto, J., Wassiljew, E., Navab, N., Falter-Wagner, C. M., and Roth, D. (2022). A virtual reality based system for the screening and classification of autism. IEEE Access, 28:2168–21785.
Silva, M. and Mulick, J. A. (2009). Diagnosticando o transtorno autista: aspectos fundamentais e considerações práticas. Psicologia: Ciência e Profissão.
Souza, I. G. S. d. and outros (2007). Dificuldades no diagnóstico de tdah em crianças. Jornal Brasileiro de Psiquiatria.
Wang, Z., Sun, Y., Shen, Q., and Cao, L. (2019). Dilated 3d convolutional neural networks for brain mri data classification. IEEE Access.
Zhang, K.-F., Yeh, S.-C., Wu, E. H.-K., Xu, X., Tsai, H.-J., and Chen, C.-C. (2024). Fusion of multi-task neurophysiological data to enhance the detection of attention-deficit/hyperactivity disorder. IEEE Journal of Translational Engineering in Health and Medicine, 12:668–674.
Zhou, Y., Gao, L., Wu, Z., Guo, X., and Chen, F. (2024). Automated multi-modal asd diagnosis using deep learning. IEEE Transactions on Medical Imaging.
Briguglio, M., Turriziani, L., Currò, A., Gagliano, A., Di Rosa, G., Caccamo, D., Tonacci, A., and Gangemi, S. (2023). A machine learning approach to the diagnosis of autism spectrum disorder and multi-systemic developmental disorder based on retrospective data and ados-2 score. Brain Sciences.
Fombonne, E. (2003). Epidemiological surveys of autism and other pervasive developmental disorders: An update. Journal of Autism and Developmental Disorders, 33(4):365–382.
Han, J., Jiang, G., Ouyang, G., and Li, X. (2022). A multimodal approach for identifying autism spectrum disorders in children. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 30:2003–2011.
Hasan, S. M. M., Uddin, M. P., Mamun, M. A., Sharif, M. I., Ulhaq, A., and Krishnamoorthy, G. (2023). A machine learning framework for early-stage detection of autism spectrum disorders. IEEE Access, 11:2168–21785.
Heinsfeld, A. S., Franco, A. R., Craddock, R. C., Buchweitz, A., and Meneguzzi, F. (2017). Identification of autism spectrum disorder using deep learning and the abide dataset. NeuroImage: Clinical.
Henderson, B., Yogarajah, P., Gardiner, B., and McGinnity, T. M. (2023). Encoding kinematic and temporal gait data in an appearance-based feature for the automatic classification of autism spectrum disorder. IEEE Access, 11:134100–134117.
Kavadi, D. P., Chirra, V. R. R., Kumar, P. R., Veesam, S. B., Yeruva, S., and Pappala, L. K. (2024). A hybrid machine learning model for accurate autism diagnosis. IEEE Access, 12:194911–194921.
Kim, B., Park, J., Kim, T., and Kwon, Y. (2023). Finding essential parts of the brain in rs-fmri can improve adhd diagnosis using deep learning. IEEE Access, 11:116065–116075.
Liang, Y., Liu, B., and Zhang, H. (2021). A convolutional neural network combined with prototype learning framework for brain functional network classification of autism spectrum disorder. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 29:2193–2202.
Martins, F. (2022). Entre 5% e 8% da população mundial apresenta transtorno de déficit de atenção com hiperatividade. [link]. Acesso em: 15 abr. 2025.
Moher, D., Liberati, A., Tetzlaff, J., and Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: The prisma statement. PLoS Medicine.
Robles, M., Namdarian, N., Otto, J., Wassiljew, E., Navab, N., Falter-Wagner, C. M., and Roth, D. (2022). A virtual reality based system for the screening and classification of autism. IEEE Access, 28:2168–21785.
Silva, M. and Mulick, J. A. (2009). Diagnosticando o transtorno autista: aspectos fundamentais e considerações práticas. Psicologia: Ciência e Profissão.
Souza, I. G. S. d. and outros (2007). Dificuldades no diagnóstico de tdah em crianças. Jornal Brasileiro de Psiquiatria.
Wang, Z., Sun, Y., Shen, Q., and Cao, L. (2019). Dilated 3d convolutional neural networks for brain mri data classification. IEEE Access.
Zhang, K.-F., Yeh, S.-C., Wu, E. H.-K., Xu, X., Tsai, H.-J., and Chen, C.-C. (2024). Fusion of multi-task neurophysiological data to enhance the detection of attention-deficit/hyperactivity disorder. IEEE Journal of Translational Engineering in Health and Medicine, 12:668–674.
Zhou, Y., Gao, L., Wu, Z., Guo, X., and Chen, F. (2024). Automated multi-modal asd diagnosis using deep learning. IEEE Transactions on Medical Imaging.
Publicado
24/04/2025
Como Citar
PEREIRA, Maria Eduarda Maia; LIMA, Fábio Ventura.
Aplicações de Machine Learning na Detecção e Monitoramento de Distúrbios de Neurodesenvolvimento. In: ESCOLA REGIONAL DE SISTEMAS DE INFORMAÇÃO DE MATO GROSSO, 1. , 2025, Cuiabá/MT.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 111-120.
DOI: https://doi.org/10.5753/ersimt.2025.8887.
