João B de O Souza Filho1, José Manoel de Seixas2, Rafael Galliez3, Basilio de Bragança Pereira3, Fernanda C de Q Mello3, Alcione Miranda Dos Santos4, Afranio Lineu Kritski3. 1. Polytechnical School (POLI), Electronics and Computer Engineering Department (DEL), Avenida Athos da Silveira Ramos, 149, Technological Center, Building H, room H-219 (room 20), Federal University of Rio de Janeiro, Rio de Janeiro, Brazil; Electrical Engineering Postgraduate Program (PPEEL), Federal Centre of Technological Education Celso Suckow da Fonseca, Rio de Janeiro, Brazil. Electronic address: jbfilho@poli.ufrj.br. 2. Polytechnical School (POLI), Electronics and Computer Engineering Department (DEL), Avenida Athos da Silveira Ramos, 149, Technological Center, Building H, room H-219 (room 20), Federal University of Rio de Janeiro, Rio de Janeiro, Brazil; Electrical Engineering Postgraduate Program (PEE), Alberto Luiz Coimbra Institute (COPPE), Federal University of Rio de Janeiro, Rio de Janeiro, Brazil. 3. Academic Tuberculosis Program, Faculty of Medicine and University Complex (HUFF and IDT), Federal University of Rio de Janeiro, Rio de Janeiro, Brazil. 4. Postgraduate Program on Collective Health, Federal University of Maranhão, Maranhão, Brazil.
Abstract
OBJECTIVES: Molecular 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. METHODS: The 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. RESULTS: MLP 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. CONCLUSIONS: In 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.
OBJECTIVES: Molecular 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. METHODS: The 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. RESULTS: MLP 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. CONCLUSIONS: In 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.
Authors: Riddhi Doshi; Dennis Falzon; Bruce V Thomas; Zelalem Temesgen; Lal Sadasivan; Giovanni Battista Migliori; Mario Raviglione Journal: ERJ Open Res Date: 2017-06-21