Machine Learning no Diagnóstico Parasitológico Direto de Leishmaniose Visceral: Uma Revisão
Resumo
A Leishmaniose Visceral Canina (LVC) é uma zoonose de grande impacto na saúde pública, cujo diagnóstico parasitológico direto exige análise microscópica detalhada, tornando-se um processo exaustivo e suscetível a erros. Técnicas de Visão Computacional (VC) e Machine Learning (ML) têm sido exploradas para automatizar esse diagnóstico, melhorando sua precisão e eficiência. Esta revisão identificou que abordagens baseadas em redes neurais profundas e segmentação de imagens demonstram alto potencial na detecção da Leishmania. No entanto, desafios como a escassez de bases de dados públicas e a limitada aplicação de imagens do exame parasitológico direto da medula óssea ainda dificultam avanços mais significativos. A implementação dessas tecnologias requer padronização e maior interpretabilidade dos modelos para garantir sua aplicabilidade na prática clínica.Referências
Álvaro, C. I. L. S. (2022). Relatório de estágio e monografia intitulada”leishmaniose canina: Sintomas e tratamentos”. Master’s thesis.
Baneth, G. (2006). Leishmaniose. In Greene: Doenças infecciosas do cão e do gato, pages 685–698. Saunders Elsevier, Saint Louis, 3ª edition.
Barbieri, C. L. (2006). Immunology of canine leishmaniasis. Parasite Immunol., 28(7):329–337.
Bermejo Rodriguez, A., Ruiz Giardin, J., Garcia Martinez, J., San Martin Lopez, J., Castaneda de la Mata, A., Lopez Lacomba, D., Jaqueti Aroca, J., and Walter, S. (2019). Diagnostic model of visceral leishmaniasis based on bone marrow findings. study of patients with clinical suspicion in which the parasite is not observed. European Journal of Internal Medicine, 69:42–49.
Campino, L. and Maia, C. (2010). Epidemiologia das leishmanioses em portugal. Acta medica portuguesa, 23(5):859–64.
Cannet, A., Simon-Chane, C., Histace, A., Akhoundi, M., Romain, O., Souchaud, M., Jacob, P., Sereno, D., Volf, P., Dvorak, V., and Sereno, D. (2023). Species identification of phlebotomine sandflies using deep learning and wing interferential pattern (wip). Scientific Reports, 13(1):21389.
Chen, C. H. (2016). Handbook of Pattern Recognition and Computer Vision. World Scientific, 5th edition.
Coelho, M., França, T., Fontoura Mateus, N., da Costa Lima Junior, M., Cena, C., and do Nascimento Ramos, C. (2023). Canine visceral leishmaniasis diagnosis by uv spectroscopy of blood serum and machine learning algorithms. Photodiagnosis and Photodynamic Therapy, 42:103575.
Costa, A. and Santos, J. (2022). Editorial: New strategies and technologies enabling point of care diagnosis of neglected or tropical diseases. Frontiers in Cellular and Infection Microbiology, 12:1089088.
e Silva, R. R. V., de Araujo, F. H. D., dos Santos, L. M. R., Veras, R. M. S., and de Medeiros, F. N. (2016). Optic disc detection in retinal images using algorithms committee with weighted voting. IEEE Latin America Transactions, 14(5):2446–2454.
Elmahallawy, E., Martínez, A., Rodríguez-Granger, J., Hoyos-Mallecot, Y., Agil, A., Mari, J., and Fernández, J. (2014). Diagnosis of leishmaniasis. The Journal of Infection in Developing Countries, 8(08):961–972.
Faria, A. R. and de Andrade, H. M. (2012). Diagnóstico da leishmaniose visceral canina: grandes avanços tecnológicos e baixa aplicação prática. Revista Pan-Amazônica de Saúde, 3(2):11–11.
Ferreira, T., Santana, E., Jacob Junior, A., Silva Junior, P., Bastos, L., Silva, A., Melo, S., Cruz, C., Aquino, V., Castro, L., Lima, G., and Freire, R. (2022). Diagnostic classification of cases of canine leishmaniasis using machine learning. Sensors, 22(9):3128.
Gonçalves, C., Borges, A., Rodrigues, A., Andrade, N., Lemus, M., Aguiar, B., and Silva, R. (2023). Computer vision in automatic visceral leishmaniasis diagnosis: a survey. IEEE Latin America Transactions, 21(2):310–319.
Gonçalves, C. d. A., Borges, A. L., Dias, V. B. L., de Andrade, N. B., Aguiar, B. G. A., and Negligenciados, P. (2022). Método automático para detecçao de leishmaniose visceral em humanos.
Gontijo, C. M. and Melo, M. N. (2004). Leishmaniose visceral no brasil: quadro atual, desafios e perspectivas. Revista Brasileira de Epidemiologia, 7(3):338–349.
Guo, W., Lv, C., Guo, M., Zhao, Q., Yin, X., and Zhang, L. (2023). Innovative applications of artificial intelligence in zoonotic disease management. Scientific One Health, 2:100045.
Ivănescu, L., Andronic, B. L., Grigore-Hristodorescu, S., Martinescu, G. V., Mîndru, R., and Miron, L. (2023). The immune response in canine and human leishmaniasis and how this influences the diagnosis-a review and assessment of recent research. Frontiers in Cellular and Infection Microbiology, 13.
Khanal, S., Pillai, M., Biswas, D., Torequl Islam, M., Verma, R., Kuca, K., Kumar, D., Najmi, A., Zoghebi, K., Khalid, A., and Mohan, S. (2024). A paradigm shift in the detection of bloodborne pathogens: conventional approaches to recent detection techniques. EXCLI Journal, 23:1245–1275.
Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele, UK, Keele University, 33(2004):1–26.
Kumar, R. and Nylén, S. (2012). Immunobiology of visceral leishmaniasis. Frontiers in immunology, 3:251.
Laraia, A. C., Santana, K., and Donato, L. E. (2018). Avaliação da resposta de anticorpos vacinais em animais vacinados contra leishmaniose visceral canina. Programa de Iniciação Científica-PIC/UniCEUB-Relatórios de Pesquisa, 4(1).
Larios, G., Ribeiro, M., Arruda, C., Oliveira, S. L., Canassa, T., Baker, M. J., Marangoni, B., Ramos, C., and Cena, C. (2021). A new strategy for canine visceral leishmaniasis diagnosis based on ftir spectroscopy and machine learning. Journal of Biophotonics, 14(11):e202100141.
Lebedev, G., Klimenko, H., Kachkovskiy, S., Konushin, V., Ryabkov, I., and Gromov, A. (2018). Application of artificial intelligence methods to recognize pathologies on medical images. Elsevier B.V., 126:1171–1177.
Marangoni-Ghoreyshi, Y., Franca, T., Esteves, J., Maranni, A., Pereira Portes, K., Cena, C., and Leal, C. (2023). Multi-resistant diarrhe-agenic escherichia coli identified by ftir and machine learning: a feasible strategy to improve the group classification. RSC Advances, 13(36):24909–24917.
Nagamori, Y., Hall Sedlak, R., DeRosa, A., Pullins, A., Cree, T., Loenser, M., Larson, B., Smith, R., and Goldstein, R. (2020). Evaluation of the vetscan imagyst: an in-clinic canine and feline fecal parasite detection system integrated with a deep learning algorithm. Parasites & Vectors, 13(1):346.
Nixon, M. and Aguado, A. (2019). Feature Extraction and Image Processing for Computer Vision. Academic Press, 4th edition.
Organização Mundial da Saúde. Leishmaniose. Acessado em 11 de janeiro de 2024.
Pan American Health Organization. Visceral leishmaniasis. [link]. Acessado em 11 de janeiro de 2024.
Parker, J. (2010). Algorithms for Image Processing and Computer Vision. Wiley Publishing, 2nd edition.
Reithinger, R. and Dujardin, J. (2007). Molecular diagnosis of leishmaniasis: current status and future applications. Journal of Clinical Microbiology, 45:21–25.
Sadeghi, A., Sadeghi, M., Fakhar, M., Zakariaei, Z., Sadeghi, M., and Bastani, R. (2024). A deep learning-based model for detecting leishmania amastigotes in microscopic slides: a new approach to telemedicine. BMC Infectious Diseases, 24(1):551.
Sakkas, H., Gartzonika, C., and Levidiotou, S. (2016). Laboratory diagnosis of human visceral leishmaniasis. Journal of Vector Borne Diseases, 53(1):8.
Salazar, J., Vera, M., Huérfano, Y., Vera, M., Gelvez-Almeida, E., and Valbuena, O. (2019). Semi-automatic detection of the evolutionary forms of visceral leishmaniasis in microscopic blood smears. Journal of Physics: Conference Series, 1386:012135.
Santos, T. T. O., Ramos, F. F., Gonçalves, I. A. P., Tavares, G. S. V., Ludolf, F., Bandeira, R. S., et al. (2021). Potential of recombinant lihyq, a novel Leishmania infantum protein, for the diagnosis of canine visceral leishmaniasis and as a diagnostic and prognostic marker for human leishmaniasis and human immunodeficiency virus co-infection: A preliminary study. Acta Tropica, 224:106126.
Scherer, R. (2020). Computer Vision Methods for Fast Image Classification and Retrieval. Springer Cham, 1st edition.
Silva, R. R., Araujo, F. H., Ushizima, D. M., Bianchi, A. G., Carneiro, C. M., and Medeiros, F. N. (2019). Radial feature descriptors for cell classification and recommendation. Journal of Visual Communication and Image Representation, 62:105–116.
Silva, S. (2007). Avaliação clínica e laboratorial de cães naturalmente infectados por Leishmania (Leishmania) chagasi (cunha & chagas, 1937), submetidos a um protocolo terapêutico em clínica veterinária de Belo Horizonte. Dissertação, Universidade Federal de Minas Gerais, Setor de Parasitologia, Belo Horizonte (MG).
Solano-Gallego, L., Llull, J., Ramos, G., Riera, C., Arboix, M., Alberola, J., and et al. (2000). The ibizian hound presents a predominantly cellular immune response against natural leishmania infection. Veterinary Parasitology, 90(1–2):37–45.
Srividya, G., Kulshrestha, A., Singh, R., and Salotra, P. (2012). Diagnosis of visceral leishmaniasis: developments over the last decade. Parasitology Research, 110(3):1065–1078.
Sundar, S. and Rai, M. (2002). Laboratory diagnosis of visceral leishmaniasis. Clinical and Vaccine Immunology, 9(5):951–958.
Sundar, S. and Singh, O. (2018). Molecular diagnosis of visceral leishmaniasis. Molecular Diagnosis & Therapy, 22(4):443–457.
Thakur, S., Joshi, J., and Kaur, S. (2020). Leishmaniasis diagnosis: an update on the use of parasitological, immunological and molecular methods. J. Parasit Dis., 44(2):253–272.
WHO, E. C. (2010). Control of the leishmaniases. World Health Organ Tech Rep Ser, 949:22–26.
Zhang, B. (2010). Computer vision vs. human vision. In 9th IEEE International Conference on Cognitive Informatics (ICCI’10), pages 3–3.
Baneth, G. (2006). Leishmaniose. In Greene: Doenças infecciosas do cão e do gato, pages 685–698. Saunders Elsevier, Saint Louis, 3ª edition.
Barbieri, C. L. (2006). Immunology of canine leishmaniasis. Parasite Immunol., 28(7):329–337.
Bermejo Rodriguez, A., Ruiz Giardin, J., Garcia Martinez, J., San Martin Lopez, J., Castaneda de la Mata, A., Lopez Lacomba, D., Jaqueti Aroca, J., and Walter, S. (2019). Diagnostic model of visceral leishmaniasis based on bone marrow findings. study of patients with clinical suspicion in which the parasite is not observed. European Journal of Internal Medicine, 69:42–49.
Campino, L. and Maia, C. (2010). Epidemiologia das leishmanioses em portugal. Acta medica portuguesa, 23(5):859–64.
Cannet, A., Simon-Chane, C., Histace, A., Akhoundi, M., Romain, O., Souchaud, M., Jacob, P., Sereno, D., Volf, P., Dvorak, V., and Sereno, D. (2023). Species identification of phlebotomine sandflies using deep learning and wing interferential pattern (wip). Scientific Reports, 13(1):21389.
Chen, C. H. (2016). Handbook of Pattern Recognition and Computer Vision. World Scientific, 5th edition.
Coelho, M., França, T., Fontoura Mateus, N., da Costa Lima Junior, M., Cena, C., and do Nascimento Ramos, C. (2023). Canine visceral leishmaniasis diagnosis by uv spectroscopy of blood serum and machine learning algorithms. Photodiagnosis and Photodynamic Therapy, 42:103575.
Costa, A. and Santos, J. (2022). Editorial: New strategies and technologies enabling point of care diagnosis of neglected or tropical diseases. Frontiers in Cellular and Infection Microbiology, 12:1089088.
e Silva, R. R. V., de Araujo, F. H. D., dos Santos, L. M. R., Veras, R. M. S., and de Medeiros, F. N. (2016). Optic disc detection in retinal images using algorithms committee with weighted voting. IEEE Latin America Transactions, 14(5):2446–2454.
Elmahallawy, E., Martínez, A., Rodríguez-Granger, J., Hoyos-Mallecot, Y., Agil, A., Mari, J., and Fernández, J. (2014). Diagnosis of leishmaniasis. The Journal of Infection in Developing Countries, 8(08):961–972.
Faria, A. R. and de Andrade, H. M. (2012). Diagnóstico da leishmaniose visceral canina: grandes avanços tecnológicos e baixa aplicação prática. Revista Pan-Amazônica de Saúde, 3(2):11–11.
Ferreira, T., Santana, E., Jacob Junior, A., Silva Junior, P., Bastos, L., Silva, A., Melo, S., Cruz, C., Aquino, V., Castro, L., Lima, G., and Freire, R. (2022). Diagnostic classification of cases of canine leishmaniasis using machine learning. Sensors, 22(9):3128.
Gonçalves, C., Borges, A., Rodrigues, A., Andrade, N., Lemus, M., Aguiar, B., and Silva, R. (2023). Computer vision in automatic visceral leishmaniasis diagnosis: a survey. IEEE Latin America Transactions, 21(2):310–319.
Gonçalves, C. d. A., Borges, A. L., Dias, V. B. L., de Andrade, N. B., Aguiar, B. G. A., and Negligenciados, P. (2022). Método automático para detecçao de leishmaniose visceral em humanos.
Gontijo, C. M. and Melo, M. N. (2004). Leishmaniose visceral no brasil: quadro atual, desafios e perspectivas. Revista Brasileira de Epidemiologia, 7(3):338–349.
Guo, W., Lv, C., Guo, M., Zhao, Q., Yin, X., and Zhang, L. (2023). Innovative applications of artificial intelligence in zoonotic disease management. Scientific One Health, 2:100045.
Ivănescu, L., Andronic, B. L., Grigore-Hristodorescu, S., Martinescu, G. V., Mîndru, R., and Miron, L. (2023). The immune response in canine and human leishmaniasis and how this influences the diagnosis-a review and assessment of recent research. Frontiers in Cellular and Infection Microbiology, 13.
Khanal, S., Pillai, M., Biswas, D., Torequl Islam, M., Verma, R., Kuca, K., Kumar, D., Najmi, A., Zoghebi, K., Khalid, A., and Mohan, S. (2024). A paradigm shift in the detection of bloodborne pathogens: conventional approaches to recent detection techniques. EXCLI Journal, 23:1245–1275.
Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele, UK, Keele University, 33(2004):1–26.
Kumar, R. and Nylén, S. (2012). Immunobiology of visceral leishmaniasis. Frontiers in immunology, 3:251.
Laraia, A. C., Santana, K., and Donato, L. E. (2018). Avaliação da resposta de anticorpos vacinais em animais vacinados contra leishmaniose visceral canina. Programa de Iniciação Científica-PIC/UniCEUB-Relatórios de Pesquisa, 4(1).
Larios, G., Ribeiro, M., Arruda, C., Oliveira, S. L., Canassa, T., Baker, M. J., Marangoni, B., Ramos, C., and Cena, C. (2021). A new strategy for canine visceral leishmaniasis diagnosis based on ftir spectroscopy and machine learning. Journal of Biophotonics, 14(11):e202100141.
Lebedev, G., Klimenko, H., Kachkovskiy, S., Konushin, V., Ryabkov, I., and Gromov, A. (2018). Application of artificial intelligence methods to recognize pathologies on medical images. Elsevier B.V., 126:1171–1177.
Marangoni-Ghoreyshi, Y., Franca, T., Esteves, J., Maranni, A., Pereira Portes, K., Cena, C., and Leal, C. (2023). Multi-resistant diarrhe-agenic escherichia coli identified by ftir and machine learning: a feasible strategy to improve the group classification. RSC Advances, 13(36):24909–24917.
Nagamori, Y., Hall Sedlak, R., DeRosa, A., Pullins, A., Cree, T., Loenser, M., Larson, B., Smith, R., and Goldstein, R. (2020). Evaluation of the vetscan imagyst: an in-clinic canine and feline fecal parasite detection system integrated with a deep learning algorithm. Parasites & Vectors, 13(1):346.
Nixon, M. and Aguado, A. (2019). Feature Extraction and Image Processing for Computer Vision. Academic Press, 4th edition.
Organização Mundial da Saúde. Leishmaniose. Acessado em 11 de janeiro de 2024.
Pan American Health Organization. Visceral leishmaniasis. [link]. Acessado em 11 de janeiro de 2024.
Parker, J. (2010). Algorithms for Image Processing and Computer Vision. Wiley Publishing, 2nd edition.
Reithinger, R. and Dujardin, J. (2007). Molecular diagnosis of leishmaniasis: current status and future applications. Journal of Clinical Microbiology, 45:21–25.
Sadeghi, A., Sadeghi, M., Fakhar, M., Zakariaei, Z., Sadeghi, M., and Bastani, R. (2024). A deep learning-based model for detecting leishmania amastigotes in microscopic slides: a new approach to telemedicine. BMC Infectious Diseases, 24(1):551.
Sakkas, H., Gartzonika, C., and Levidiotou, S. (2016). Laboratory diagnosis of human visceral leishmaniasis. Journal of Vector Borne Diseases, 53(1):8.
Salazar, J., Vera, M., Huérfano, Y., Vera, M., Gelvez-Almeida, E., and Valbuena, O. (2019). Semi-automatic detection of the evolutionary forms of visceral leishmaniasis in microscopic blood smears. Journal of Physics: Conference Series, 1386:012135.
Santos, T. T. O., Ramos, F. F., Gonçalves, I. A. P., Tavares, G. S. V., Ludolf, F., Bandeira, R. S., et al. (2021). Potential of recombinant lihyq, a novel Leishmania infantum protein, for the diagnosis of canine visceral leishmaniasis and as a diagnostic and prognostic marker for human leishmaniasis and human immunodeficiency virus co-infection: A preliminary study. Acta Tropica, 224:106126.
Scherer, R. (2020). Computer Vision Methods for Fast Image Classification and Retrieval. Springer Cham, 1st edition.
Silva, R. R., Araujo, F. H., Ushizima, D. M., Bianchi, A. G., Carneiro, C. M., and Medeiros, F. N. (2019). Radial feature descriptors for cell classification and recommendation. Journal of Visual Communication and Image Representation, 62:105–116.
Silva, S. (2007). Avaliação clínica e laboratorial de cães naturalmente infectados por Leishmania (Leishmania) chagasi (cunha & chagas, 1937), submetidos a um protocolo terapêutico em clínica veterinária de Belo Horizonte. Dissertação, Universidade Federal de Minas Gerais, Setor de Parasitologia, Belo Horizonte (MG).
Solano-Gallego, L., Llull, J., Ramos, G., Riera, C., Arboix, M., Alberola, J., and et al. (2000). The ibizian hound presents a predominantly cellular immune response against natural leishmania infection. Veterinary Parasitology, 90(1–2):37–45.
Srividya, G., Kulshrestha, A., Singh, R., and Salotra, P. (2012). Diagnosis of visceral leishmaniasis: developments over the last decade. Parasitology Research, 110(3):1065–1078.
Sundar, S. and Rai, M. (2002). Laboratory diagnosis of visceral leishmaniasis. Clinical and Vaccine Immunology, 9(5):951–958.
Sundar, S. and Singh, O. (2018). Molecular diagnosis of visceral leishmaniasis. Molecular Diagnosis & Therapy, 22(4):443–457.
Thakur, S., Joshi, J., and Kaur, S. (2020). Leishmaniasis diagnosis: an update on the use of parasitological, immunological and molecular methods. J. Parasit Dis., 44(2):253–272.
WHO, E. C. (2010). Control of the leishmaniases. World Health Organ Tech Rep Ser, 949:22–26.
Zhang, B. (2010). Computer vision vs. human vision. In 9th IEEE International Conference on Cognitive Informatics (ICCI’10), pages 3–3.
Publicado
09/06/2025
Como Citar
DIAS, Viviane B. L. et al.
Machine Learning no Diagnóstico Parasitológico Direto de Leishmaniose Visceral: Uma Revisão. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 25. , 2025, Porto Alegre/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 641-652.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2025.7695.
