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 work 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 non-infected, Leishmania infantum, and Trypanosoma evansi infected groups were studied. The data demonstrate that principal component analysis with machine learning algorithms achieved an overall accuracy above 85% in the diagnosis.
A New Strategy for Canine Visceral Leishmaniasis Diagnosis Based on Molecular Spectroscopy and Machine Learning
