Please use this identifier to cite or link to this item: https://www.arca.fiocruz.br/handle/icict/1805
Title: Towards a precise test for malaria diagnosis in the Brazilian Amazon: comparison among field microscopy, a rapid diagnostic test, nested PCR, and a computational expert system based on artificial neural networks
Authors: Andrade, Bruno de Bezerril
Reis Filho, Antonio José Souza
Barros Júnior, Austeclino Magalhães
Souza Neto, Sebastião Martins
Nogueira, Lucas L
Fukutani, Kiyoshi Ferreira
Camargo, Erney P
Camargo, Luís Marcelo Aranha
Barral, Aldina Maria Prado
Duarte, Ângelo
Barral Netto, Manoel
Affilliation: Fundação Oswaldo Cruz. Centro de Pesquisas Gonçalo Moniz. Salvador, BA, Brasil
Fundação Oswaldo Cruz. Centro de Pesquisas Gonçalo Moniz. Salvador, BA, Brasil
Faculdade Ruy Barbosa. Departamento de Ciência da Computação. Salvador, BA, Brasil
Fundação Oswaldo Cruz. Centro de Pesquisas Gonçalo Moniz. Salvador, BA, Brasil
Fundação Oswaldo Cruz. Centro de Pesquisas Gonçalo Moniz. Salvador, BA, Brasil
Fundação Oswaldo Cruz. Centro de Pesquisas Gonçalo Moniz. Salvador, BA, Brasil
Universidade de São Paulo. Instituto de Ciências Biológicas. Unidade Avançada de Pesquisa. Porto Velho, RO, Brasil
Universidade de São Paulo. Instituto de Ciências Biológicas. Unidade Avançada de Pesquisa. Porto Velho, RO, Brasil
Fundação Oswaldo Cruz. Centro de Pesquisas Gonçalo Moniz. Salvador, BA, Brasil
Universidade Estadual de Feira de Santana. Departamento de Tecnologia. Feira de Santana, BA, Brasil
Fundação Oswaldo Cruz. Centro de Pesquisas Gonçalo Moniz. Salvador, BA, Brasil
Abstract: BACKGROUND: Accurate malaria diagnosis is mandatory for the treatment and management of severe cases. Moreover, individuals with asymptomatic malaria are not usually screened by health care facilities, which further complicates disease control efforts. The present study compared the performances of a malaria rapid diagnosis test (RDT), the thick blood smear method and nested PCR for the diagnosis of symptomatic malaria in the Brazilian Amazon. In addition, an innovative computational approach was tested for the diagnosis of asymptomatic malaria. METHODS: The study was divided in two parts. For the first part, passive case detection was performed in 311 individuals with malaria-related symptoms from a recently urbanized community in the Brazilian Amazon. A cross-sectional investigation compared the diagnostic performance of the RDT Optimal-IT, nested PCR and light microscopy. The second part of the study involved active case detection of asymptomatic malaria in 380 individuals from riverine communities in Rondônia, Brazil. The performances of microscopy, nested PCR and an expert computational system based on artificial neural networks (MalDANN) using epidemiological data were compared. RESULTS: Nested PCR was shown to be the gold standard for diagnosis of both symptomatic and asymptomatic malaria because it detected the major number of cases and presented the maximum specificity. Surprisingly, the RDT was superior to microscopy in the diagnosis of cases with low parasitaemia. Nevertheless, RDT could not discriminate the Plasmodium species in 12 cases of mixed infections (Plasmodium vivax + Plasmodium falciparum). Moreover, the microscopy presented low performance in the detection of asymptomatic cases (61.25% of correct diagnoses). The MalDANN system using epidemiological data was worse that the light microscopy (56% of correct diagnoses). However, when information regarding plasma levels of interleukin-10 and interferon-gamma were inputted, the MalDANN performance sensibly increased (80% correct diagnoses). CONCLUSIONS: An RDT for malaria diagnosis may find a promising use in the Brazilian Amazon integrating a rational diagnostic approach. Despite the low performance of the MalDANN test using solely epidemiological data, an approach based on neural networks may be feasible in cases where simpler methods for discriminating individuals below and above threshold cytokine levels are available.
DeCS: Malaria
Microscopia
Redes Neurais (Computação)
Plasmodium
Reação em Cadeia da Polimerase
Plasmodium
Sistemas Especialistas
Issue Date: 2010
Citation: ANDRADE, B. B. et al. Towards a precise test for malaria diagnosis in the Brazilian Amazon: comparison among field microscopy, a rapid diagnostic test, nested PCR, and a computational expert system based on artificial neural networks. Malaria Journal, v. 9, p. 117, 2010.
DOI: 10.1186/1475-2875-9-117
Copyright: open access
Appears in Collections:BA - IGM - Artigos de Periódicos

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