SynthesisPLoS neglected tropical diseases2022
Machine learning and deep learning techniques to support clinical diagnosis of arboviral diseases: A systematic review.
Synthesis in PLoS neglected tropical diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 3 of them syntheses that pooled it.
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Who cites it
24 citing papers in PubMed, 3 syntheses or guidelines pooled it, 60 citations in OpenAlex.
- Performance and clinical utility of image-based machine learning models for the diagnosis of neglected tropical diseases in low- and middle-income countries: a systematic review.BMC infectious diseases · 2026Pooled it
- On the usage of artificial intelligence in leprosy care: A systematic literature review.PLoS computational biology · 2025Pooled it
- On usage of artificial intelligence for predicting mortality during and post-pregnancy: a systematic review of literature.BMC medical informatics and decision making · 2022Pooled it
- Multi-angle, cross-domain fusion strategy enhances automated insect identification and hierarchical categorization: a case study on assassin bugs (Hemiptera: Reduviidae).Cladistics : the international journal of the Willi Hennig Society · 2026Article
- Anti-Dengue IgG Seroprevalence and Exposure-Related Risk in Italian Military Personnel Deployed on Overseas Missions: A Cross-Sectional Study.Tropical medicine and infectious disease · 2026Article
- Chronic phase of Chikungunya: understanding the impact of joint pain using data science and artificial intelligence.Tropical medicine and health · 2026Article
- AI-Assisted Differentiation of Dengue and Chikungunya Using Big, Imbalanced Epidemiological Data.Tropical medicine and infectious disease · 2026Article
- Proof-of-Concept Machine Learning Framework for Arboviral Disease Classification Using Literature-Derived Synthetic Data: Methodological Development Preceding Clinical Validation.Healthcare (Basel, Switzerland) · 2026Article
- Arthralgia and fever as dominant predictors of Chikungunya confirmation: an explainable artificial intelligence approach.Frontiers in medicine · 2026Article
- Forecasting and Early Warning Systems for Dengue Outbreaks: Updated Narrative Review.Revista da Sociedade Brasileira de Medicina Tropical · 2026Review
- Role of artificial intelligence in screening and medical imaging of precancerous gastric diseases.World journal of clinical oncology · 2025Review
- Prediction of Cerebrospinal Fluid (CSF) Pressure with Generative Adversarial Network Synthetic Plasma-CSF Biomarker Pairing.Neuroinformatics · 2025Article
- Detection and Identification of Food-Borne Yeasts: An Overview of the Relevant Methods and Their Evolution.Microorganisms · 2025Review
- Article
- Serodynamics: A primer and synthetic review of methods for epidemiological inference using serological data.Epidemics · 2024Review
- Methodology for the Differential Classification of Dengue and Chikungunya According to the PAHO 2022 Diagnostic Guide.Viruses · 2024Article
- Quantitative detection of chikungunya, Zika, and dengue viruses by one-step real-time PCR in different cell substrates.Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology] · 2024Article
- Precision Prediction for Dengue Fever in Singapore: A Machine Learning Approach Incorporating Meteorological Data.Tropical medicine and infectious disease · 2024Article
- Artificial Intelligence: A Next-Level Approach in Confronting the COVID-19 Pandemic.Healthcare (Basel, Switzerland) · 2023Review
- Detection of acute dengue virus infection, with and without concurrent malaria infection, in a cohort of febrile children in Kenya, 2014-2019, by clinicians or machine learning algorithms.PLOS global public health · 2023Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNeglected 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.
objectiveThe 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.
methodWe 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.
resultsResults 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.
conclusionsThe 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.