title: Prediction of lymph node parasite load from clinical data in dogs with leishmaniasis: An application of radial basis artificial neural networks.
abstract: Quantification 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.
authors.affiliation: São Paulo State University (Unesp). School of Veterinary Medicine, Araçatuba. Department of Clinics, Surgery and Animal Reproduction. São Paulo, Brazil.
authors.forename: Rafaela Beatriz Pintor
authors.initials: RB
authors.lastname: Torrecilha
authors.affiliation: São Paulo State University (Unesp). School of Agricultural and Veterinarian Sciences, Jaboticabal. Department of Preventative Veterinary Medicine and Animal Reproduction. São Paulo, Brazil.
authors.forename: Yuri Tani
authors.initials: YT
authors.lastname: Utsunomiya
authors.affiliation: USP - Universidade de São Paulo, Departamento de Patologia Veterinária, Faculdade de Medicina Veterinária e Zootecnia, São Paulo, Brazil.
authors.forename: Luís Fábio da Silva
authors.initials: LF
authors.lastname: Batista
authors.affiliation: São Paulo State University (Unesp). School of Veterinary Medicine, Araçatuba. Department of Clinics, Surgery and Animal Reproduction. São Paulo, Brazil.
authors.forename: Anelise Maria
authors.initials: AM
authors.lastname: Bosco
authors.affiliation: São Paulo State University (Unesp). School of Veterinary Medicine, Araçatuba. Department of Support, Production and Animal Health. São Paulo, Brazil.
authors.forename: Cáris Maroni
authors.initials: CM
authors.lastname: Nunes
authors.affiliation: São Paulo State University (Unesp). School of Veterinary Medicine, Araçatuba. Department of Clinics, Surgery and Animal Reproduction. São Paulo, Brazil.
authors.forename: Paulo César
authors.initials: PC
authors.lastname: Ciarlini
authors.affiliation: USP - Universidade de São Paulo, Departamento de Patologia Veterinária, Faculdade de Medicina Veterinária e Zootecnia, São Paulo, Brazil. Electronic address: mdlauren@usp.br.
authors.forename: Márcia Dalastra
authors.initials: MD
authors.lastname: Laurenti
mesh_terms: D000818:Animals; D004283:Dog Diseases / Q000175:diagnosis / Q000469:parasitology* / Q000473:pathology; D004285:Dogs; D007896:Leishmaniasis / Q000175:diagnosis / Q000469:parasitology / Q000473:pathology / Q000662:veterinary*; D008198:Lymph Nodes / Q000469:parasitology*; D016571:Neural Networks, Computer; D059208:Parasite Load / Q000662:veterinary*
keywords: Canis lupus familiaris; Leishmania spp.; Machine learning; qPCR