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Revisión de métodos de aprendizaje automático para detectar al parásito de la enfermedad de Chagas
DOI:
https://doi.org/10.33064/iycuaa2020803008Palabras clave:
enfermedad de Chagas, Trypanosoma cruzi, detección, segmentación, aprendizaje profundo, red neuronal convolucionalResumen
La enfermedad de Chagas, causada por el parásito Trypanosoma cruzi, afecta a una gran cantidad de personas en Latinoamérica. Un análisis de sangre resulta el método preferido para generar un diagnóstico de la enfermedad; sin embargo, es un proceso tardado, ya que requiere de mucho esfuerzo de expertos para analizar grandes cantidades de muestras en búsqueda de parásitos. La implementación de sistemas automáticos que faciliten la detección del parásito en imágenes de muestras de sangre capturadas por microscopio es de gran utilidad. Por tanto, en este artículo de revisión se detallan los diferentes trabajos científicos que utilizan técnicas de aprendizaje automático para detectar y segmentar al parásito Trypanosoma cruzi en imágenes digitales.
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• Dong, Y., Jiang, Z., Shen, H., & Pan, W. D. (2017a). Classification accuracies of malaria infected cells using deep convolutional neural networks based on decompressed images. SoutheastCon 2017, Charlotte, NC, US, 1-6. doi: 10.1109/SECON.2017.7925268
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Biomedical & Health Informatics (BHI), Orlando, Florida, US, 101-104. doi: 10.1109/BHI.2017.7897215
• Duda, R. O., Hart, P. E., & Stork, D. G. (2001). Pattern Classification (2nd ed.). NY, US: Wiley-Interscience New York.
• Egüez, K. E., Alonso-Padilla, J., Terán, C., Chipana, Z., García, W., Torrico, F., … Pinazo, M. J. (2017). Rapid diagnostic tests duo as alternative to conventional serological assays for conclusive Chagas disease diagnosis. PLoS Neglected Tropical Diseases, 11(4). doi: 10.1371/journal.pntd.0005501
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11(3), e201700003. doi: 10.1002/jbio.201700003
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53-62). Springer. doi: 10.1007/978-3-319-94544-6_6
• Guhl, F. (marzo de 2008). Photograph showing an adult specimen of Triatoma dimidiata from Colombia [Fotografía]. Recuperado de https://es.wikipedia.org/wiki/Archivo:Triatoma_dimidiata-adult.jpg
• Liang, Z., Powell, A., Ersoy, I., Poostchi, M., Silamut, K., Palaniappan, K., Thoma, G. (2016). CNN-based image analysis for malaria diagnosis. 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 493-496. doi: 10.1109/
BIBM.2016.7822567
• Magoulas, G. D., & Prentza, A. (2001). Machine learning in medical applications. In G. Paliouras, V. Karkaletsis, & C. Spyropoulos (Eds.), Machine Learning and Its Applications (pp. 300-307). doi: 10.1007/3-540-44673-7_19
• MedlinePlus. (s. f.). Examen de sangre ELISA [Especificaciones]. Recuperado de https://medlineplus.gov/spanish/ency/article/003332.htm
• Mehanian, C., Jaiswal, M., Delahunt, C., Thompson, C., Horning, M., Hu, L., … Bell, D. (2017). Computer-automated malaria diagnosis and quantitation using convolutional neural networks. 2017 IEEE International Conference on Computer Vision
Workshops (ICCVW), 116-125. doi: 10.1109/ICCVW.2017.22
• Peñas, K. E. D., Rivera, P. T., & Naval, P. C. (2017). Malaria parasite detection and species identification on thin blood smears using a convolutional neural network. 2017 IEEE/ACM International Conference on Connected Health: Applications,
Systems and Engineering Technologies (CHASE), 1-6. doi: 10.1109/CHASE.2017.51
• Ponce, C., Ponce, E., Vinelli, E., Montoya, A., Aguilar, V., Gonzalez, A., … Silveira, J. (2005). Validation of a rapid and reliable test for diagnosis of Chagas’ disease by detection of Trypanosoma cruzi-specific antibodies in blood of donors and patients in Central America. Journal of Clinical Microbiology, 43(10), 5065 -5068. doi: 10.1128/JCM.43.10.5065-5068.2005
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Health Care, 56, 271-281. Recuperado de https://arxiv.org/abs/1608.02989
• Razzak, M. I., Naz, S., & Zaib, A. (2018). Deep learning for medical image processing: Overview, challenges and the future. In N. Dey, A. Ashour, & S. Borra (Eds.), Classification in BioApps (pp. 323-350). Springer. doi: 10.1007/978-3-319-65981-7_12
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10.1007/s11517-006-0044-2
• Sidey-Gibbons, J. A. M., & Sidey-Gibbons, C. J. (2019). Machine learning in medicine: A practical introduction. BMC Medical Research Methodology, 19(64). doi: 10.1186/s12874-019-0681-4
• Soberanis-Mukul, R. (2012). Detección de Trypanosoma cruzi en imágenes obtenidas a partir de muestras sanguíneas (Tesis de pregrado inédita). Universidad Autónoma de Yucatán, Mérida, Yucatán.
• __________ (2014). Algoritmos de segmentación de Trypanosoma cruzi en imágenes de muestras sanguíneas (Tesis de maestría inédita). Universidad Autónoma de Yucatán, Mérida, Yucatán.
• Soberanis-Mukul, R., Uc-Cetina, V., Brito-Loeza, C., & Ruiz-Piña, H. (2013). An automatic algorithm for the detection of Trypanosoma cruzi parasites in blood sample images. Computer Methods and Programs in Biomedicine, 112(3), 633-639. doi:
10.1016/j.cmpb.2013.07.013
• Uc-Cetina, V., Brito-Loeza, C., & Ruiz-Piña, H. (2013). Chagas parasites detection through gaussian discriminant analysis. Abstraction and Application, 8, 6-17. Recuperado de redi. uady.mx:8080/bitstream/handle/123456789/770/UcBrito-
Ruiz_2013.pdf
• __________ (2015). Chagas parasite detection in blood images using adaboost. Computational and Mathematical Methods in Medicine, 2015. doi: 10.1155/2015/139681
• Vapnik, V. N. (1998). Statistical learning theory. Wiley.
• Viola, P. & Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. Proceedings of the 2001 IEEEComputer Society Conference on Computer Vision and Pattern Recognition CVPR 2001. Kauai, HI, US, 511-518. doi:
10.1109/CVPR.2001.990517
• World Health Organization. (s. f.). Chagas disease (American trypanosomiasis) [Ficha informativa]. Recuperado de https://www.who.int/chagas/disease/en/
• __________ (2020). Chagas disease (also known as American tripanosomiasis) [Datos clave]. Recuperado de https://www.who.int/en/news-room/fact-sheets/detail/chagas-disease-(american-trypanosomiasis)
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2020-06-30
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Las obras publicadas en versión electrónica de la revista están bajo la licencia Creative Commons Atribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)
