Prediction of lymph node parasite load from clinical data in dogs with leishmaniasis: An application of radial basis artificial neural networks
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...
Published in: | Veterinary Parasitology |
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Online Access: | http://hdl.handle.net/11449/173972 https://doi.org/10.1016/j.vetpar.2016.12.016 |
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ftunivespir:oai:repositorio.unesp.br:11449/173972 2023-07-02T03:31:55+02:00 Prediction of lymph node parasite load from clinical data in dogs with leishmaniasis: An application of radial basis artificial neural networks Torrecilha, Rafaela Beatriz Pintor Utsunomiya, Yuri Tani Batista, Luís Fábio da Silva Bosco, Anelise Maria Nunes, Cáris Maroni Ciarlini, Paulo César Laurenti, Márcia Dalastra Universidade Estadual Paulista (UNESP) 2017-01-30 13-18 http://hdl.handle.net/11449/173972 https://doi.org/10.1016/j.vetpar.2016.12.016 eng eng Veterinary Parasitology 1,275 http://dx.doi.org/10.1016/j.vetpar.2016.12.016 Veterinary Parasitology, v. 234, p. 13-18. 1873-2550 0304-4017 http://hdl.handle.net/11449/173972 doi:10.1016/j.vetpar.2016.12.016 2-s2.0-85007029631 2-s2.0-85007029631.pdf 3613940018299500 orcid:0000-0003-1480-5208 openAccess Canis lupus familiaris Leishmania spp Machine learning qPCR info:eu-repo/semantics/article 2017 ftunivespir https://doi.org/10.1016/j.vetpar.2016.12.016 2023-06-12T17:07:32Z 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. Article in Journal/Newspaper Canis lupus Universidade Estadual Paulista São Paulo: Repositório Institucional UNESP Veterinary Parasitology 234 13 18 |
institution |
Open Polar |
collection |
Universidade Estadual Paulista São Paulo: Repositório Institucional UNESP |
op_collection_id |
ftunivespir |
language |
English |
topic |
Canis lupus familiaris Leishmania spp Machine learning qPCR |
spellingShingle |
Canis lupus familiaris Leishmania spp Machine learning qPCR Torrecilha, Rafaela Beatriz Pintor Utsunomiya, Yuri Tani Batista, Luís Fábio da Silva Bosco, Anelise Maria Nunes, Cáris Maroni Ciarlini, Paulo César Laurenti, Márcia Dalastra Prediction of lymph node parasite load from clinical data in dogs with leishmaniasis: An application of radial basis artificial neural networks |
topic_facet |
Canis lupus familiaris Leishmania spp Machine learning qPCR |
description |
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. |
author2 |
Universidade Estadual Paulista (UNESP) |
format |
Article in Journal/Newspaper |
author |
Torrecilha, Rafaela Beatriz Pintor Utsunomiya, Yuri Tani Batista, Luís Fábio da Silva Bosco, Anelise Maria Nunes, Cáris Maroni Ciarlini, Paulo César Laurenti, Márcia Dalastra |
author_facet |
Torrecilha, Rafaela Beatriz Pintor Utsunomiya, Yuri Tani Batista, Luís Fábio da Silva Bosco, Anelise Maria Nunes, Cáris Maroni Ciarlini, Paulo César Laurenti, Márcia Dalastra |
author_sort |
Torrecilha, Rafaela Beatriz Pintor |
title |
Prediction of lymph node parasite load from clinical data in dogs with leishmaniasis: An application of radial basis artificial neural networks |
title_short |
Prediction of lymph node parasite load from clinical data in dogs with leishmaniasis: An application of radial basis artificial neural networks |
title_full |
Prediction of lymph node parasite load from clinical data in dogs with leishmaniasis: An application of radial basis artificial neural networks |
title_fullStr |
Prediction of lymph node parasite load from clinical data in dogs with leishmaniasis: An application of radial basis artificial neural networks |
title_full_unstemmed |
Prediction of lymph node parasite load from clinical data in dogs with leishmaniasis: An application of radial basis artificial neural networks |
title_sort |
prediction of lymph node parasite load from clinical data in dogs with leishmaniasis: an application of radial basis artificial neural networks |
publishDate |
2017 |
url |
http://hdl.handle.net/11449/173972 https://doi.org/10.1016/j.vetpar.2016.12.016 |
genre |
Canis lupus |
genre_facet |
Canis lupus |
op_relation |
Veterinary Parasitology 1,275 http://dx.doi.org/10.1016/j.vetpar.2016.12.016 Veterinary Parasitology, v. 234, p. 13-18. 1873-2550 0304-4017 http://hdl.handle.net/11449/173972 doi:10.1016/j.vetpar.2016.12.016 2-s2.0-85007029631 2-s2.0-85007029631.pdf 3613940018299500 orcid:0000-0003-1480-5208 |
op_rights |
openAccess |
op_doi |
https://doi.org/10.1016/j.vetpar.2016.12.016 |
container_title |
Veterinary Parasitology |
container_volume |
234 |
container_start_page |
13 |
op_container_end_page |
18 |
_version_ |
1770271354215464960 |