Screening for active pulmonary tuberculosis: Development and applicability of artificial neural network models.

Journal: Tuberculosis (Edinburgh, Scotland)
PMID: 30029922

Abstract

Tuberculosis (TB) remains a significant public health challenge, motivated by the diversity of healthcare epidemiological settings, as other factors. Cost-effective screening has substantial importance for TB control, demanding new diagnostic tools. This paper proposes a decision support tool (DST) for screening pulmonary TB (PTB) patients at a secondary clinic. The DST is composed of an adaptive resonance model (iART) for risk group identification (low, medium and high) and a multilayer perceptron (MLP) neural network for classifying patients as active or inactive PTB. Our tool attains an overall sensitivity (SE) and specificity (SP) of 92% (95% CI; 79-97) and 58% (95% CI; 47-68), respectively. SE values for smear-positive and smear-negative patients are 96% (95% CI; 80-99) and 82% (95% CI; 52-95), as well as higher than 83% (95% CI; 43-97) in low and high-risk cases. Even in scenarios with prevalence up to 20%, negative predictive values superior to 95% are obtained. The proposed DST provides a quick and low-cost pretest for presumptive PTB patients, which is useful to guide confirmatory testing and patient management, especially in settings with limited resources in low and middle-incoming countries.

Authors

  • João Baptista de Oliveira E Souza Filho
    Electrical Engineering Program, Department of Electronics and Computer Engineering, COPPE/POLI, Federal University of Rio de Janeiro, Brazil. Electronic address: jbfilho@poli.ufrj.br.
  • Mauro Sanchez
    Department of Public Health, School of Health Sciences, University of Brasilia, Brazil; Centre for Operational Research, The International Union Against Tuberculosis and Lung Disease, Paris, France. Electronic address: maurosanchez@unb.br.
  • José Manoel de Seixas
    Electrical Engineering Program, Department of Electronics and Computer Engineering, COPPE/POLI, Federal University of Rio de Janeiro, Brazil; Signal Processing Lab, Electrical Engineering Program, Alberto Coimbra Institute, Polytechnic School, Federal University of Rio de Janeiro, Brazil. Electronic address: seixas@lps.ufrj.br.
  • Carmen Maidantchik
    Signal Processing Lab, Electrical Engineering Program, Alberto Coimbra Institute, Polytechnic School, Federal University of Rio de Janeiro, Brazil. Electronic address: lodi@lps.ufrj.br.
  • Rafael Galliez
    Tuberculosis Academic Program, Medical School, Federal University of Rio de Janeiro, Brazil. Electronic address: galliez77@gmail.com.
  • Adriana da Silva Rezende Moreira
    Tuberculosis Academic Program, Medical School, Federal University of Rio de Janeiro, Brazil. Electronic address: rezendemoreira.adriana@gmail.com.
  • Paulo Albuquerque da Costa
    Tuberculosis Academic Program, Medical School, Federal University of Rio de Janeiro, Brazil. Electronic address: albuquerquepc@terra.com.br.
  • Martha Maria Oliveira
    Tuberculosis Academic Program, Medical School, Federal University of Rio de Janeiro, Brazil. Electronic address: martholiveira@yahoo.com.br.
  • Anthony David Harries
    Centre for Operational Research, The International Union Against Tuberculosis and Lung Disease, Paris, France. Electronic address: adharries@theunion.org.
  • Afrânio Lineu Kritski
    Tuberculosis Academic Program, Medical School, Federal University of Rio de Janeiro, Brazil. Electronic address: kritskia@gmail.com.

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