A new strategy for canine visceral leishmaniasis diagnosis based on FTIR spectroscopy and machine learning. Issue 11 (26th August 2021) - Larios, Gustavo; Ribeiro, Matheus; Arruda, Carla; Oliveira, Samuel L.; Canassa, Thalita; Baker, Matthew J.; Marangoni, Bruno; Ramos, Carlos; Cena, Cícero
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. Abstract : A new, fast, and accurate method for Leishmaniasis diagnosis in dogs was developed by using canine blood serum infrared absorption data and artificial intelligence. Traditional tests usually show low specificity due to cross‐reaction with other diseases, high cost. The new method is free of chemical reagents, easy to handle, low cost, and high specificity.