title: Artificial neural network models to support the diagnosis of pleural tuberculosis in adult patients.
abstract: BACKGROUND
  Clinicians in countries with high tuberculosis (TB) prevalence often treat pleural TB based on clinical grounds, as the availability and sensitivity of diagnostic tests are poor.
  
  
  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.
authors.affiliation: Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia/Poli, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
authors.forename: J M
authors.initials: JM
authors.lastname: Seixas
authors.forename: J
authors.initials: J
authors.lastname: Faria
authors.forename: J B O
authors.initials: JB
authors.lastname: Souza Filho
authors.forename: A F M
authors.initials: AF
authors.lastname: Vieira
authors.forename: A
authors.initials: A
authors.lastname: Kritski
authors.forename: A
authors.initials: A
authors.lastname: Trajman
mesh_terms: D000328:Adult; D001431:Bacteriological Techniques; D001706:Biopsy; D060085:Coinfection; D003936:Diagnosis, Computer-Assisted; D016002:Discriminant Analysis; D018450:Disease Progression; D042241:Early Diagnosis; D015658:HIV Infections / Q000175:diagnosis; D006801:Humans; D016014:Linear Models; D009169:Mycobacterium tuberculosis / Q000302:isolation & purification; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D019152:Paracentesis; D010996:Pleural Effusion / Q000382:microbiology; D011237:Predictive Value of Tests; D011379:Prognosis; D011446:Prospective Studies; D014396:Tuberculosis, Pleural / Q000175:diagnosis* / Q000382:microbiology