Artificial neural network models to support the diagnosis of pleural tuberculosis in adult patients
OBJECTIVE: To evaluate the role of artificial neural networks (ANN) as an aid for the non-invasive diagnosis of pleural TB. These tools can be used in simple computer devices (tablets) without remote internet connection.
METHODS: The clinical history and human immunodeficiency virus (HIV) status of 137 patients were prospectively entered in a database. Both non-linear ANN and the linear Fisher discriminant were used to calculate performance indexes based on clinical grounds. The same procedure was performed including pleural fluid test results (smear, culture, adenosine deaminase, serology and nucleic acid amplification test). The gold standard was any positive test for TB.
RESULTS: In pre-test modelling, the neural model reached >90% accuracy (Fisher discriminant 74.5%). Under pre-test conditions, ANN had better accuracy compared to each test considered separately.
CONCLUSIONS: ANN are highly reliable for diagnosing pleural TB based on clinical grounds and HIV status only, and are useful even in remote conditions lacking access to sophisticated medical or computer infrastructure. In other better-equipped scenarios, these tools should be evaluated as substitutes for thoracocentesis and pleural biopsy.
Document Type: Research Article
Affiliations: 1: Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia/Poli, Federal University of Rio de Janeiro, Rio de Janeiro, Rio de Janeiro, Brazil 2: Federal Centre of Technological Education Celso Suckov da Fonseca, Rio de Janeiro, Rio de Janeiro, Brazil 3: Health Education Post-graduation Program, Gama Filho University, Rio de Janeiro, Rio de Janeiro, Brazil 4: Tuberculosis Academic Program, Medical School, Federal University of Rio de Janeiro, Rio de Janeiro, Rio de Janeiro, Brazil 5: Health Education Post-graduation Program, Gama Filho University, Rio de Janeiro, Rio de Janeiro, Brazil; and McGill University Medical School, Montreal, Quebec, Canada
Publication date: 2013-05-01
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