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

Filho, João Baptista de Oliveira e Souza; Sanchez, Mauro Niskier; Seixas, J. M.; Maidantchik, C.; Galliez, Rafael Mello; Moreira, Adriana da Silva Rezende; Costa, Paulo Albuquerque da; Oliveira, Martha Maria de; Harries, Anthony; Kritski, Afrânio Lineu

doi:10.1016/j.tube.2018.05.012

Cited by 16 publications

(6 citation statements)

References 26 publications

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“…Em países com alta carga de tuberculose, como o Brasil, há poucos estudos prospectivos nos quais se analisam as características clínicas, microbiológicas e radiográficas de pacientes com TBP ou o papel do DM e pré-DM nos desfechos do tratamento da TBP. (8,10) Em nosso estudo, a frequência de DM em pacientes com TBP (14,2%) foi maior do que a relatada em outro estudo realizado no Brasil (8) e mais próxima das relatadas em outras séries (de 12,8% a 25%), (16)(17)(18)(19) porém menor do que a relatada em um estudo realizado na Índia, (15) no qual a proporção de pacientes com tuberculose e DM foi de 30%. Em países como a China (20) e o Quênia, (21) a frequência de DMTB é menor (5,0% e 6,3%, respectivamente) do que a observada em nosso estudo.…”

Section: Discussionunclassified

“…Uma amostra consecutiva de conveniência composta por indivíduos que procuraram o centro com histórico de no mínimo duas semanas de tosse foi avaliada por meio de um escore clínico (10) baseado em sinais e sintomas respiratórios de tuberculose. A probabilidade de TBP nos pacientes cuja pontuação foi ≥ 5 era média/alta e, portanto, foram convidados a participar do estudo.…”

Section: Métodosunclassified

“…A probabilidade de TBP nos pacientes cuja pontuação foi ≥ 5 era média/alta e, portanto, foram convidados a participar do estudo. (10) Foram incluídos no estudo os pacientes com idade ≥ 18 anos que concordaram em ser entrevistados e se submeter a medidas antropométricas, radiografia de tórax (RXT) e coleta de amostras clínicas. Foram considerados pacientes com TBP os participantes que apresentaram cultura de escarro com resultado positivo para M. tuberculosis e/ou teste Xpert MTB/RIF (GeneXpert; Cepheid, Sunnyvale, CA, EUA) com resultado positivo em amostras de escarro.…”

Section: Métodosunclassified

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Clinical, laboratory, and radiographic aspects of patients with pulmonary tuberculosis and dysglycemia and tuberculosis treatment outcomes

et al. 2022

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Objective: To analyze the association of dysglycemia with clinical, laboratory, and radiographic characteristics of patients with pulmonary tuberculosis (PTB), as well as with their tuberculo-sis treatment outcomes. Methods: This was a longitudinal study involving 140 patients diag-nosed with PTB (positive cultures for Mycobacterium tuberculosis or positive Xpert MTB/RIF results from sputum samples). Patients were evaluated at diagnosis (M0), after completing the second month of treatment (M2), and at the end of treatment (MEND). At M0, the pati-ents were classified into three groups: normoglycemia+PTB (NGTB); pre-diabetes melli-tus+PTB (PDMTB), and diabetes mellitus+PTB (DMTB), in accordance with glycated hemoglo-bin levels (< 5.7%, 5.7%-6.4%, and = 6.5%, respectively). Treatment outcomes were classified as favorable (cure or treatment completion) and unfavorable (death, loss to follow-up, or treatment failure). Results: In our sample, 76 patients (61.4%) had dysglycemia, 20 of whom (14.3%) had DM at M0. The patients with dysglycemia, in comparison with those in the NGTB group, more frequently presented with positive sputum smear microscopy (94.2% vs. 75.9%; p = 0.003); cavities (80.2% vs. 63.0%; p = 0.03); bilateral lesions (67.4% vs. 46.0%; p = 0.02); and higher median of affected thirds of the lungs (3.0 vs. 2.0; p = 0.03) on chest radiography. No significant differences regarding outcomes were found among the groups, but tuberculosis lethality was higher in the DMTB group than in the PDMTB and NGTB groups (20% vs. 2.2%). Conclusions: PTB patients with dysglycemia had laboratory and radiographic manifestations indicative of more advanced disease, and the risk of death was higher in the DMTB group. These findings reinforce the recommendation for early screening for DM in patients with newly diagnosed tuberculosis in order to reduce the risk of death during treatment.

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Clinical, laboratory, and radiographic aspects of patients with pulmonary tuberculosis and dysglycemia and tuberculosis treatment outcomes

et al. 2022

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“…The ANs are placed in several layers; the input layer receives the variables, which are processed to subsequent hidden layers consecutively, until the output layer reaches a final prediction ( 20 ). The interactions between each layer are dependent on synaptic weights, which are determined by an iterative processing of the input variables ( 21 ). Using different mathematical methods, the synaptic weights are gradually adjusted to achieve a better predictive performance during the training and validation process ( 22 ).…”

Section: Discussionmentioning

confidence: 99%

Diagnostic Value and Effectiveness of an Artificial Neural Network in Biliary Atresia

et al. 2020

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Objectives: Biliary atresia (BA) is a devastating pediatric liver disease. Early diagnosis is important for timely intervention and better prognosis. Using clinical parameters for non-invasive and efficient BA diagnosis, we aimed to establish an artificial neural network (ANN). Methods: A total of 2,384 obstructive jaundice patients from 2012 to 2017 and their 137 clinical parameters were screened for eligibility. A standard binary classification feed-forward ANN was employed. The network was trained and validated for accuracy. Gamma-glutamyl transpeptidase (GGT) level was used as an independent predictor and a comparison to assess the network effectiveness. Results: We included 46 parameters and 1,452 patients for ANN modeling. Total bilirubin, direct bilirubin, and GGT were the most significant indicators. The network consisted of an input layer, 3 hidden layers with 12 neurons each, and an output layer. The network showed good predictive property with a high area under curve (AUC) (0.967, sensitivity 97.2% and specificity 91.0%). Five-fold cross validation showed the mean accuracy for training data of 93.2% and for validation data of 88.6%. Conclusions: The high accuracy and efficiency demonstrated by the ANN model is promising in the noninvasive diagnosis of BA and could be considered as in a low-cost and independent expert diagnosis system.

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“…In the present study, three machine learning tools were established to predict T2DM development, including an artificial neural network of the multilayer perceptron (MLP) model, an XGboost model and a support vector machine (SVM) model and combined 6 clinical features and 10 bacterial species. The SVM model has been reported to predict chronic kidney disease in clinical applications (19), the XGBoost model exhibits improved performance in predicting patients with postoperative sepsis (20), and the MLP model performed well when applied to computed tomography for coronary artery disease and myocardial perfusion (21). K-fold is a common cross validation approach, particularly when the datasets are limited (22).…”

Section: Fecal Sample Collection and Dna Extractionmentioning

confidence: 99%

Application of machine learning tools: Potential and useful approach for the prediction of type 2 diabetes mellitus based on the gut microbiome profile

Zhang

et al. 2022

Exp Ther Med

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The gut microbiota plays an important role in the regulation of the immune system and the metabolism of the host. The aim of the present study was to characterize the gut microbiota of patients with type 2 diabetes mellitus (T2DM). A total of 118 participants with newly diagnosed T2DM and 89 control subjects were recruited in the present study; six clinical parameters were collected and the quantity of 10 different types of bacteria was assessed in the fecal samples using quantitative PCR. Taking into consideration the six clinical variables and the quantity of the 10 different bacteria, 3 predictive models were established in the training set and test set, and evaluated using a confusion matrix, area under the receiver operating characteristic curve (AUC) values, sensitivity (recall), specificity, accuracy, positive predictive value and negative predictive value (npv). The abundance of Bacteroides, Eubacterium rectale and Roseburia inulinivorans was significantly lower in the T2DM group compared with the control group. However, the abundance of Enterococcus was significantly higher in the T2DM group compared with the control group. In addition, Faecalibacterium prausnitzii, Enterococcus and Roseburia inulinivorans were significantly associated with sex status while Bacteroides, Bifidobacterium, Enterococcus and Roseburia inulinivorans were significantly associated with older age. In the training set, among the three models, support vector machine (SVM) and XGboost models obtained AUC values of 0.72 and 0.70, respectively. In the test set, only SVM obtained an AUC value of 0.77, and the precision and specificity were both above 0.77, whereas the accuracy, recall and npv were above 0.60. Furthermore, Bifidobacterium, age and Roseburia inulinivorans played pivotal roles in the model. In conclusion, the SVM model exhibited the highest overall predictive power, thus the combined use of machine learning tools with gut microbiome profiling may be a promising approach for improving early prediction of T2DM in the near feature.

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

Cited by 16 publications

References 26 publications

Clinical, laboratory, and radiographic aspects of patients with pulmonary tuberculosis and dysglycemia and tuberculosis treatment outcomes

Clinical, laboratory, and radiographic aspects of patients with pulmonary tuberculosis and dysglycemia and tuberculosis treatment outcomes

Diagnostic Value and Effectiveness of an Artificial Neural Network in Biliary Atresia

Application of machine learning tools: Potential and useful approach for the prediction of type 2 diabetes mellitus based on the gut microbiome profile

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