A new strategy for canine visceral leishmaniasis diagnosis based on FTIR spectroscopy and machine learning |
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| Authors: | Gustavo Larios Matheus Ribeiro Carla Arruda Samuel L. Oliveira Thalita Canassa Matthew J. Baker Bruno Marangoni Carlos Ramos Cícero Cena |
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| Affiliation: | 1. Grupo de Óptica e Fotônica, Instituto de Física, Universidade Federal de Mato Grosso do Sul, Campo Grande, Brazil;2. Laboratório de Parasitologia Humana, Instituto de Biociências, Universidade Federal de Mato Grosso do Sul, Campo Grande, Brazil;3. Pure and Applied Chemistry, University of Stratchclyde, Technology and Innovation Centre, Glasgow, UK;4. Faculdade de Medicina Veterinária e Zootecnia, Universidade Federal de Mato Grosso do Sul, Campo Grande, Brazil |
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| Abstract: | Visceral leishmaniasis is a neglected disease caused by protozoan parasites of the genus Leishmania. The successful control of the disease depends on its accurate and early diagnosis, which is usually made by combining clinical symptoms with laboratory tests such as serological, parasitological, and molecular tests. However, early diagnosis based on serological tests may exhibit low accuracy due to lack of specificity caused by cross-reactivities with other pathogens, and sensitivity issues related, among other reasons, to disease stage, leading to misdiagnosis. In this study was investigated the use of mid-infrared spectroscopy and multivariate analysis to perform a fast, accurate, and easy canine visceral leishmaniasis diagnosis. Canine blood sera of 20 noninfected, 20 Leishmania infantum, and eight Trypanosoma evansi infected dogs were studied. The data demonstrate that principal component analysis with machine learning algorithms achieved an overall accuracy above 85% in the diagnosis. |
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| Keywords: | biofluids diagnosis FTIR spectroscopy machine learning visceral leishmaniasis |
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