Torrecilha, Rafaela Beatriz Pintor [UNESP]Utsunomiya, Yuri Tani [UNESP]Batista, Luís Fábio da SilvaBosco, Anelise Maria [UNESP]Nunes, Cáris Maroni [UNESP]Ciarlini, Paulo César [UNESP]Laurenti, Márcia Dalastra2018-12-112018-12-112017-01-30Veterinary Parasitology, v. 234, p. 13-18.1873-25500304-4017http://hdl.handle.net/11449/173972Quantification of Leishmania infantum load via real-time quantitative polymerase chain reaction (qPCR) in lymph node aspirates is an accurate tool for diagnostics, surveillance and therapeutics follow-up in dogs with leishmaniasis. However, qPCR requires infrastructure and technical training that is not always available commercially or in public services. Here, we used a machine learning technique, namely Radial Basis Artificial Neural Network, to assess whether parasite load could be learned from clinical data (serological test, biochemical markers and physical signs). By comparing 18 different combinations of input clinical data, we found that parasite load can be accurately predicted using a relatively small reference set of 35 naturally infected dogs and 20 controls. In the best case scenario (use of all clinical data), predictions presented no bias or inflation and an accuracy (i.e., correlation between true and predicted values) of 0.869, corresponding to an average error of ±38.2 parasites per unit of volume. We conclude that reasonable estimates of L. infantum load from lymph node aspirates can be obtained from clinical records when qPCR services are not available.13-18engCanis lupus familiarisLeishmania spp.Machine learningqPCRArtigo10.1016/j.vetpar.2016.12.016Acesso aberto2-s2.0-850070296312-s2.0-85007029631.pdf36139400182995000000-0003-1480-5208