A screening system for smear-negative pulmonary tuberculosis using artificial neural networks

•Diagnostic tests show low sensitivity for smear-negative pulmonary tuberculosis.•Prognostic and risk assessment models using artificial neural networks are proposed.•The decision support system is useful to expedite complementary examinations and for screening.•This system uses multilayer perceptron and inspired adaptive resonance theory models.•An accuracy of 88% was achieved using only signs and symptoms.

SummaryObjectivesMolecular tests show low sensitivity for smear-negative pulmonary tuberculosis (PTB). A screening and risk assessment system for smear-negative PTB using artificial neural networks (ANNs) based on patient signs and symptoms is proposed.MethodsThe prognostic and risk assessment models exploit a multilayer perceptron (MLP) and inspired adaptive resonance theory (iART) network. Model development considered data from 136 patients with suspected smear-negative PTB in a general hospital.ResultsMLP showed higher sensitivity (100%, 95% confidence interval (CI) 78–100%) than the other techniques, such as support vector machine (SVM) linear (86%; 95% CI 60–96%), multivariate logistic regression (MLR) (79%; 95% CI 53–93%), and classification and regression tree (CART) (71%; 95% CI 45–88%). MLR showed a slightly higher specificity (85%; 95% CI 59–96%) than MLP (80%; 95% CI 54–93%), SVM linear (75%, 95% CI 49–90%), and CART (65%; 95% CI 39–84%). In terms of the area under the receiver operating characteristic curve (AUC), the MLP model exhibited a higher value (0.918, 95% CI 0.824–1.000) than the SVM linear (0.796, 95% CI 0.651–0.970) and MLR (0.782, 95% CI 0.663–0.960) models. The significant signs and symptoms identified in risk groups are coherent with clinical practice.ConclusionsIn settings with a high prevalence of smear-negative PTB, the system can be useful for screening and also to aid clinical practice in expediting complementary tests for higher risk patients.