Cargando…

Machine learning and deep learning techniques to support clinical diagnosis of arboviral diseases: A systematic review

BACKGROUND: Neglected tropical diseases (NTDs) primarily affect the poorest populations, often living in remote, rural areas, urban slums or conflict zones. Arboviruses are a significant NTD category spread by mosquitoes. Dengue, Chikungunya, and Zika are three arboviruses that affect a large propor...

Descripción completa

Detalles Bibliográficos
Autores principales: da Silva Neto, Sebastião Rogério, Tabosa Oliveira, Thomás, Teixeira, Igor Vitor, Aguiar de Oliveira, Samuel Benjamin, Souza Sampaio, Vanderson, Lynn, Theo, Endo, Patricia Takako
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8791518/
https://www.ncbi.nlm.nih.gov/pubmed/35025860
http://dx.doi.org/10.1371/journal.pntd.0010061
_version_ 1784640201611542528
author da Silva Neto, Sebastião Rogério
Tabosa Oliveira, Thomás
Teixeira, Igor Vitor
Aguiar de Oliveira, Samuel Benjamin
Souza Sampaio, Vanderson
Lynn, Theo
Endo, Patricia Takako
author_facet da Silva Neto, Sebastião Rogério
Tabosa Oliveira, Thomás
Teixeira, Igor Vitor
Aguiar de Oliveira, Samuel Benjamin
Souza Sampaio, Vanderson
Lynn, Theo
Endo, Patricia Takako
author_sort da Silva Neto, Sebastião Rogério
collection PubMed
description BACKGROUND: Neglected tropical diseases (NTDs) primarily affect the poorest populations, often living in remote, rural areas, urban slums or conflict zones. Arboviruses are a significant NTD category spread by mosquitoes. Dengue, Chikungunya, and Zika are three arboviruses that affect a large proportion of the population in Latin and South America. The clinical diagnosis of these arboviral diseases is a difficult task due to the concurrent circulation of several arboviruses which present similar symptoms, inaccurate serologic tests resulting from cross-reaction and co-infection with other arboviruses. OBJECTIVE: The goal of this paper is to present evidence on the state of the art of studies investigating the automatic classification of arboviral diseases to support clinical diagnosis based on Machine Learning (ML) and Deep Learning (DL) models. METHOD: We carried out a Systematic Literature Review (SLR) in which Google Scholar was searched to identify key papers on the topic. From an initial 963 records (956 from string-based search and seven from a single backward snowballing procedure), only 15 relevant papers were identified. RESULTS: Results show that current research is focused on the binary classification of Dengue, primarily using tree-based ML algorithms. Only one paper was identified using DL. Five papers presented solutions for multi-class problems, covering Dengue (and its variants) and Chikungunya. No papers were identified that investigated models to differentiate between Dengue, Chikungunya, and Zika. CONCLUSIONS: The use of an efficient clinical decision support system for arboviral diseases can improve the quality of the entire clinical process, thus increasing the accuracy of the diagnosis and the associated treatment. It should help physicians in their decision-making process and, consequently, improve the use of resources and the patient’s quality of life.
format Online
Article
Text
id pubmed-8791518
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-87915182022-01-27 Machine learning and deep learning techniques to support clinical diagnosis of arboviral diseases: A systematic review da Silva Neto, Sebastião Rogério Tabosa Oliveira, Thomás Teixeira, Igor Vitor Aguiar de Oliveira, Samuel Benjamin Souza Sampaio, Vanderson Lynn, Theo Endo, Patricia Takako PLoS Negl Trop Dis Research Article BACKGROUND: Neglected tropical diseases (NTDs) primarily affect the poorest populations, often living in remote, rural areas, urban slums or conflict zones. Arboviruses are a significant NTD category spread by mosquitoes. Dengue, Chikungunya, and Zika are three arboviruses that affect a large proportion of the population in Latin and South America. The clinical diagnosis of these arboviral diseases is a difficult task due to the concurrent circulation of several arboviruses which present similar symptoms, inaccurate serologic tests resulting from cross-reaction and co-infection with other arboviruses. OBJECTIVE: The goal of this paper is to present evidence on the state of the art of studies investigating the automatic classification of arboviral diseases to support clinical diagnosis based on Machine Learning (ML) and Deep Learning (DL) models. METHOD: We carried out a Systematic Literature Review (SLR) in which Google Scholar was searched to identify key papers on the topic. From an initial 963 records (956 from string-based search and seven from a single backward snowballing procedure), only 15 relevant papers were identified. RESULTS: Results show that current research is focused on the binary classification of Dengue, primarily using tree-based ML algorithms. Only one paper was identified using DL. Five papers presented solutions for multi-class problems, covering Dengue (and its variants) and Chikungunya. No papers were identified that investigated models to differentiate between Dengue, Chikungunya, and Zika. CONCLUSIONS: The use of an efficient clinical decision support system for arboviral diseases can improve the quality of the entire clinical process, thus increasing the accuracy of the diagnosis and the associated treatment. It should help physicians in their decision-making process and, consequently, improve the use of resources and the patient’s quality of life. Public Library of Science 2022-01-13 /pmc/articles/PMC8791518/ /pubmed/35025860 http://dx.doi.org/10.1371/journal.pntd.0010061 Text en © 2022 da Silva Neto et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
da Silva Neto, Sebastião Rogério
Tabosa Oliveira, Thomás
Teixeira, Igor Vitor
Aguiar de Oliveira, Samuel Benjamin
Souza Sampaio, Vanderson
Lynn, Theo
Endo, Patricia Takako
Machine learning and deep learning techniques to support clinical diagnosis of arboviral diseases: A systematic review
title Machine learning and deep learning techniques to support clinical diagnosis of arboviral diseases: A systematic review
title_full Machine learning and deep learning techniques to support clinical diagnosis of arboviral diseases: A systematic review
title_fullStr Machine learning and deep learning techniques to support clinical diagnosis of arboviral diseases: A systematic review
title_full_unstemmed Machine learning and deep learning techniques to support clinical diagnosis of arboviral diseases: A systematic review
title_short Machine learning and deep learning techniques to support clinical diagnosis of arboviral diseases: A systematic review
title_sort machine learning and deep learning techniques to support clinical diagnosis of arboviral diseases: a systematic review
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8791518/
https://www.ncbi.nlm.nih.gov/pubmed/35025860
http://dx.doi.org/10.1371/journal.pntd.0010061
work_keys_str_mv AT dasilvanetosebastiaorogerio machinelearninganddeeplearningtechniquestosupportclinicaldiagnosisofarboviraldiseasesasystematicreview
AT tabosaoliveirathomas machinelearninganddeeplearningtechniquestosupportclinicaldiagnosisofarboviraldiseasesasystematicreview
AT teixeiraigorvitor machinelearninganddeeplearningtechniquestosupportclinicaldiagnosisofarboviraldiseasesasystematicreview
AT aguiardeoliveirasamuelbenjamin machinelearninganddeeplearningtechniquestosupportclinicaldiagnosisofarboviraldiseasesasystematicreview
AT souzasampaiovanderson machinelearninganddeeplearningtechniquestosupportclinicaldiagnosisofarboviraldiseasesasystematicreview
AT lynntheo machinelearninganddeeplearningtechniquestosupportclinicaldiagnosisofarboviraldiseasesasystematicreview
AT endopatriciatakako machinelearninganddeeplearningtechniquestosupportclinicaldiagnosisofarboviraldiseasesasystematicreview