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

作者：Sebastião Rogério da Silva Neto, Thomás Tabosa de Oliveira, Igor Vitor Teixeira, Samuel Benjamin Aguiar de Oliveira, Vanderson de Souza Sampaio, Theo Lynn, Patrícia Takako Endo · 发表于：PLoS neglected tropical diseases · 年份：2022 · DOI：10.1371/journal.pntd.0010061 · 被引用次数：70 · 研究领域：Mosquito-borne diseases and control、Digital Imaging for Blood Diseases、COVID-19 diagnosis using AI

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 differentia...