Sistemas Expertos y su Impacto en el Control y Evaluación del Desarrollo en Niños: Revisión Sistemática de Literatura

• Gloria Paredes-Mamani ^[1]
  1. [1] Universidad Nacional Mayor San Marcos Lima, Perú Facultad de Ingeniería de Sistema e Informática, Unidad de Postgrado
• Localización: RISTI: Revista Ibérica de Sistemas e Tecnologias de Informação, ISSN-e 1646-9895, Nº. 55, 2024, págs. 87-109
• Idioma: portugués
• DOI: 10.17013/risti.55.87-109
• Títulos paralelos:
  • Expert Systems and their Impact on Developmental Screening and Assessment in Children: A Systematic Literature Review
• Enlaces
  • Texto completo (pdf)
• Resumen
  • español

    El control y desarrollo infantil asistido por sistemas expertos es esencial para la detección, diagnóstico y prevención en la salud de los niños, contribuyen significativamente a la accesibilidad, precisión y calidad de la atención infantil. Esta revisión analiza el impacto de los sistemas expertos en el control y evaluación del desarrollo en niños, desde 2017 a 2023. La estrategia de búsqueda identificó 22,164 estudios de fuentes como Web of Science, Scopus, ProQuest, Science Direct, Wiley Online Library, EBSCO host y ETH Zúrich. Tras aplicar criterios rigurosos de exclusión de búsqueda, se evidencia los estudios que proporcionan un aporte significativo son procedentes de Asia, los sistemas expertos fortalecen el diagnóstico y control de 52 enfermedades que afectan la salud infantil, de las cuales son 49 aportan a la salud física y 11 al área de salud mental. La revisión evidencia impacto significativo en la atención al niño es en el diagnóstico, prevención y evaluación.

  • English

    Expert system-assisted child development and monitoring is essential for screening, diagnosis and prevention in children’s health, contributing significantly to the accessibility, accuracy and quality of childcare. This review analyses the impact of expert systems on child developmental screening and assessment from 2017 to 2023. The search strategy identified 22,164 studies from sources such as Web of Science, Scopus, ProQuest, Science Direct, Wiley Online Library, EBSCO host and ETH Zurich. After applying rigorous search exclusion criteria, it is evident that the studies that provide a significant contribution are from Asia, expert systems strengthen the diagnosis and control of 52 diseases that affect children’s health, of which 49 contribute to physical health and 11 to the area of mental health. The review shows significant impact on childcare in terms of diagnosis, prevention and evaluation.

• Referencias bibliográficas
  • Abdel Hameed, M., Hassaballah, M., Hosney, M. E., & Alqahtani, A. (2022). An AI-enabled Internet of Things based autism care system for...
  • Abir, W. H., Uddin, M. F., Khanam, F. R., Tazin, T., Khan, M. M., Masud, M., & Aljahdali, S. (2022). Explainable AI in diagnosing and...
  • Annapragada, A. V., Donaruma, M. M. K., Annapragada, A. V., & Starosolski, Z. A. (2021). A natural language processing and deep learning...
  • Arnold, R. W. (2022). Comparative validation of PlusoptiX and AI-Optic photoscreeners in children with high amblyopia risk factor prevalence....
  • Bala, M., Ali, M. H., Satu, M. S., Hasan, K. F., & Moni, M. A. (2022). Efficient machine learning models for early stage detection of...
  • Beaulac, C., Rosenthal, J. S., Pei, Q., Friedman, D., Wolden, S., & Hodgson, D. (2020). An evaluation of machine learning techniques to...
  • Boivin, V., Shahriari, M., Faure, G., Mellul, S., Tiassou, E. D., Jouvet, P., & Noumeir, R. (2023). Multimodality video acquisition system...
  • Bondi-Kelly, E., Chen, H., Golden, C. D., Behari, N., & Tambe, M. (2023). Predicting micronutrient deficiency with publicly available...
  • Carrascal, A. I. O., Cotte, D. S., Arango, N. A. R., & Vélez, A. F. P. (2019). Descubrimiento de conocimiento en historias clínicas mediante...
  • Chang, T. H., Liu, Y. C., Lin, S. R., Chiu, P. H., Chou, C. C., Chang, L. Y., & Lai, F. P. (2023). Clinical characteristics of hospitalized...
  • Chen, X., You, G., Chen, Q., Zhang, X., Wang, N., He, X., Zhu, L., Li, Z., Liu, C., Yao, S., Ge, J., Gao, W., & Yu, H. (2023). Development...
  • Chen, Y., Lv, Q., Andrinopoulou, E. R., Gallardo-Estrella, L., Charbonnier, J. P., Caudri, D., Davis, S. D., Rosenfeld, M., Ratjen, F., Kronmal,...
  • Choi, J. W., Cho, Y. J., Ha, J. Y., Lee, Y. Y., Koh, S. Y., Seo, J. Y., Choi, Y. H., Cheon, J. E., Phi, J. H., Kim, I., Yang, J., & Kim,...
  • Cirillo, M. D., Mirdell, R., Sjöberg, F., & Pham, T. D. (2021). Improving burn depth assessment for pediatric scalds by AI based on semantic...
  • Cordeiro, J. R., Mosca, S., Correia-Costa, A., Ferreira, C., Pimenta, J., Correia-Costa, L., Barros, H., & Postolache, O. (2023). The...
  • Correa, M., Zimic, M., Barrientos, F., Barrientos, R., Román-Gonzalez, A., Pajuelo, M. J., Anticona, C., Mayta, H., Alva, A., Solis-Vasquez,...
  • Duran, I., Stark, C., Saglam, A., Semmelweis, A., Lioba Wunram, H., Spiess, K., & Schoenau, E. (2022). Artificial intelligence to improve...
  • Esposito, S., Bianchini, S., Argentiero, A., Gobbi, R., Vicini, C., & Principi, N. (2021). New approaches and technologies to improve...
  • Goulooze, S. C., Zwep, L. B., Vogt, J. E., Krekels, E. H. J., Hankemeier, T., van den Anker, J. N., & Knibbe, C. A. J. (2020). Beyond...
  • Gupta, K., Jiwani, N., Pau, G., & Alibakhshikenari, M. (2023). A machine learning approach using statistical models for early detection...
  • Hadri, S. A., & Bouramoul, A. (2023). Towards a deep learning based contextual chatbot for preventing depression in young children with...
  • Huang, Y., Li, Q., Yang, Q., Huang, Z., Gao, H., Xu, Y., & Liao, L. (2021). Early prediction of refractory epilepsy in children under...
  • Iadanza, E., Goretti, F., Sorelli, M., Melillo, P., Pecchia, L., Simonelli, F., & Gherardelli, M. (2020). Automatic detection of genetic...
  • Ji, Y., Choi, T. Y., Lee, J., Yoon, S., Jeong, H., Kang, S. W., Kim, J. W., & Won, G. H. (2022). Characteristics of attention-deficit/hyperactivity...
  • Ji, Y., Zhi, X., Wu, Y., Zhang, Y., Yang, Y., Peng, T., & Ji, L. (2023). Regression analysis of air pollution and pediatric respiratory...
  • Jiang, X., Yu, J., Ye, J., Jia, W., Xu, W., & Shu, Q. (2023). A deep learning-based method for pediatric congenital heart disease detection...
  • Khosravi, P., Huck, N. A., Shahraki, K., Hunter, S. C., Danza, C. N., Kim, S. Y., Forbes, B. J., Dai, S., Levin, A. V., Binenbaum, G., Chang,...
  • Kim, B. J., Kim, B. S., Mun, J. H., Lim, C., & Kim, K. H. (2022). An accurate deep learning model for wheezing in children using real...
  • Kim, D. W., Ahn, H. G., Kim, J., Yoon, C. S., Kim, J. H., & Yang, S. (2021). Advanced kidney volume measurement method using ultrasonography...
  • Lam, J. Y., Shimizu, C., Tremoulet, A. H., Bainto, E., Roberts, S. C., Sivilay, N., Gardiner, M. A., Kanegaye, J. T., Hogan, A. H., Salazar,...
  • Leon-Ayala, R., & Andrade-Arenas, L. (2022). Mobile application design: Caries health care for children. International Journal of Interactive...
  • Li, H., Huang, Z., Fu, J., Li, Y., Zeng, N., Zhang, J., Ye, C., & Jin, L. (2019). Modified weights-and-structure-determination neural...
  • Linares-Espinós, E., Hernández, V., Domínguez-Escrig, J. L., Fernández-Pello, S., Hevia, V., Mayor, J., Padilla-Fernández, B., & Ribal,...
  • Loh, H. W., Ooi, C. P., Oh, S. L., Barua, P. D., Tan, Y. R., Molinari, F., March, S., Acharya, U. R., & Fung, D. S. S. (2023). Deep neural...
  • Masad, I. S., Alqudah, A., Alqudah, A. M., & Almashaqbeh, S. (2021). A hybrid deep learning approach towards building an intelligent system...
  • Nel, S., Feucht, U. D., Nel, A. L., Becker, P. J., & Wenhold, F. A. M. (2022). A novel screening tool to predict severe acute malnutrition...
  • Nguyen, T., Maarek, R., Hermann, A. L., Kammoun, A., Marchi, A., Khelifi-Touhami, M. R., Collin, M., Jaillard, A., Kompel, A. J., Hayashi,...
  • Nishat, F., Hudson, S., Panesar, P., Ali, S., Litwin, S., Zeller, F., Candelaria, P., Foster, M. E., & Stinson, J. (2023). Exploring the...
  • Nogales, A., García-Tejedor, Á. J., Chazarra, P., & Ugalde-Canitrot, A. (2023). Discriminating and understanding brain states in children...
  • Paolucci, C., Giorgini, F., Scheda, R., Alessi, F. V., & Diciotti, S. (2023). Early prediction of Autism Spectrum Disorders through interaction...
  • Pavez, R., Diaz, J., Arango-Lopez, J., Ahumada, D., Mendez-Sandoval, C., & Moreira, F. (2023). Emo-mirror: A proposal to support emotion...
  • Rasheed, M. A., Chand, P., Ahmed, S., Sharif, H., Hoodbhoy, Z., Siddiqui, A., & Hasan, B. S. (2021). Use of artificial intelligence on...
  • Reismann, J., Romualdi, A., Kiss, N., Minderjahn, M. I., Kallarackal, J., Schad, M., & Reismann, M. (2019). Diagnosis and classification...
  • Seo, H., Hwang, J. J., Jung, Y. H., Lee, E., Nam, O. H., & Shin, J. (2023). Deep focus approach for accurate bone age estimation from...
  • Shekhar, R., Vanama, G., John, T., Issac, J., Arjoune, Y., & Doroshow, R. W. (2022). Automated identification of innocent Still’s murmur...
  • Shi, Y., Fu, J., Zeng, M., Ge, Y., Wang, X., Xia, A., Shen, W., Wang, J., Chen, W., Jiang, S., & Zhai, X. (2023). Information technology...
  • Sierra, I., Díaz-Díaz, N., Barranco, C., & Carrasco-Villalón, R. (2022). Artificial Intelligence-Assisted Diagnosis for Early Intervention...
  • Tragomalou, A., Moschonis, G., Manios, Y., Kassari, P., Ioakimidis, I., Diou, C., Stefanopoulos, L., Lekka, E., Maglaveras, N., Delopoulos,...
  • Vaezipour, N., Fritschi, N., Brasier, N., Bélard, S., Domínguez, J., Tebruegge, M., Portevin, D., & Ritz, N. (2022). Towards accurate...
  • Vaquerizo-Villar, F., Gutiérrez-Tobal, G. C., Calvo, E., Álvarez, D., Kheirandish-Gozal, L., del Campo, F., Gozal, D., & Hornero, R. (2023)....
  • Villegas-Ch, W., Urbina-Camacho, I., & García-Ortiz, J. (2023). Detection of abnormal patterns in children’s handwriting by using an artificial-intelligence-based...
  • von Elling-Tammen, L., Stark, C., Wloka, K. R., Alberg, E., Schoenau, E., & Duran, I. (2023). Predicting gross motor function in children...
  • Wall, D. P., Liu-Mayo, S., Salomon, C., Shannon, J., & Taraman, S. (2023). Optimizing a de novo artificial intelligence-based medical...
  • Wang, J., Dou, J., Han, J., Li, G., & Tao, J. (2023). A population-based study to assess two convolutional neural networks for dental...
  • Wu, T. J., Tsai, C. L., Gao, Q. Z., Chen, Y. P., Kuo, C. F., & Huang, Y. H. (2022). The application of artificial-intelligence-assisted...
  • Wu, Y., Zhao, S., Yang, X., Yang, C., Shi, Z., Liu, Q., Wang, Y., Qin, M., & Zhang, L. (2022). Ultrasound lung image under artificial...
  • Xu, W., Shu, L., Gong, P., Huang, C., Xu, J., Zhao, J., Shu, Q., Zhu, M., Qi, G., Zhao, G., & Yu, G. (2022). A deep-learning aided diagnostic...
  • Yang, Q., Hu, D., Wang, T., Cao, J., Dong, F., Gao, W., Jiang, T., & Gao, F. (2022). Childhood epilepsy syndromes classification based...
  • Yasin, S., Draz, U., Ali, T., Shahid, K., Abid, A., Bibi, R., Irfan, M., Huneif, M. A., Almedhesh, S. A., Alqahtani, S. M., Abdulwahab, A.,...
  • Zhang, Q., & Wan, N. J. (2022). Simple method to predict insulin resistance in children aged 6–12 years by using machine learning. Diabetes,...
  • Zhao, X., Zhang, M., Cheng, M., Yue, X., Li, W., & Li, C. (2022). Construction of artificial intelligence system of carpal bone age for...