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

Filho, João Baptista de Oliveira e Souza; Seixas, J. M.; Galliez, Rafael Mello; Pereira, Basílio de Bragança; Mello, Fernanda Cq; Santos, Alcione Miranda dos; Kritski, Afrânio Lineu

doi:10.1016/j.ijid.2016.05.019

Cited by 18 publications

(5 citation statements)

References 29 publications

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“…SVM is an ensemble machine learning to improve classification performance compared with a single classifier, which has also been applied in the prediction of disease progression such as breast cancer [ 57 ]. MLP is very famous for its autonomic learning capacity without the requirement of previous knowledge, which has also been used in the diagnosis of TB [ 58 ] and assessment of prognostic risk for SNPT patients [ 59 ]. Our research indicated the best classification performance of MLP for simultaneously identifying the SPPT, SNPT, and controls, with the highest accuracy of 94.74%, suggesting the advantage of MLP over RF and SVM to some extent.…”

Section: Discussionmentioning

confidence: 99%

Combining metabolome and clinical indicators with machine learning provides some promising diagnostic markers to precisely detect smear-positive/negative pulmonary tuberculosis

Wang

et al. 2022

BMC Infect Dis

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Background Tuberculosis (TB) had been the leading lethal infectious disease worldwide for a long time (2014–2019) until the COVID-19 global pandemic, and it is still one of the top 10 death causes worldwide. One important reason why there are so many TB patients and death cases in the world is because of the difficulties in precise diagnosis of TB using common detection methods, especially for some smear-negative pulmonary tuberculosis (SNPT) cases. The rapid development of metabolome and machine learning offers a great opportunity for precision diagnosis of TB. However, the metabolite biomarkers for the precision diagnosis of smear-positive and smear-negative pulmonary tuberculosis (SPPT/SNPT) remain to be uncovered. In this study, we combined metabolomics and clinical indicators with machine learning to screen out newly diagnostic biomarkers for the precise identification of SPPT and SNPT patients. Methods Untargeted plasma metabolomic profiling was performed for 27 SPPT patients, 37 SNPT patients and controls. The orthogonal partial least squares-discriminant analysis (OPLS-DA) was then conducted to screen differential metabolites among the three groups. Metabolite enriched pathways, random forest (RF), support vector machines (SVM) and multilayer perceptron neural network (MLP) were performed using Metaboanalyst 5.0, “caret” R package, “e1071” R package and “Tensorflow” Python package, respectively. Results Metabolomic analysis revealed significant enrichment of fatty acid and amino acid metabolites in the plasma of SPPT and SNPT patients, where SPPT samples showed a more serious dysfunction in fatty acid and amino acid metabolisms. Further RF analysis revealed four optimized diagnostic biomarker combinations including ten features (two lipid/lipid-like molecules and seven organic acids/derivatives, and one clinical indicator) for the identification of SPPT, SNPT patients and controls with high accuracy (83–93%), which were further verified by SVM and MLP. Among them, MLP displayed the best classification performance on simultaneously precise identification of the three groups (94.74%), suggesting the advantage of MLP over RF/SVM to some extent. Conclusions Our findings reveal plasma metabolomic characteristics of SPPT and SNPT patients, provide some novel promising diagnostic markers for precision diagnosis of various types of TB, and show the potential of machine learning in screening out biomarkers from big data.

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Section: Discussionmentioning

confidence: 99%

Combining metabolome and clinical indicators with machine learning provides some promising diagnostic markers to precisely detect smear-positive/negative pulmonary tuberculosis

Wang

et al. 2022

BMC Infect Dis

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“…Some studies have shown that levels of CPR and MLR relate to various diseases. 19–23 CPR can stratify the risk degree for patients with acute heart failure. 20 It has also been shown that esophageal squamous carcinoma patients with higher preoperative MLR tend to have an unfavorable prognosis.…”

Section: Discussionmentioning

confidence: 99%

“…With the continuous advancement of research methods, methods of machine learning, such as nomogram, are widely applied for differential diagnosis and early identification of various diseases. 19 The present study aimed to distinguish APTB from IPTB in patients with positive T-SPOT by constructing and validating a prediction model based on a combination of coagulation and inflammatory indicators. The prediction model was further transformed into a scoring system to facilitate clinical practice.…”

Section: Introductionmentioning

confidence: 99%

Construction and Validation of a Diagnostic Scoring System for Predicting Active Pulmonary Tuberculosis in Patients with Positive T-SPOT Based on Indicators Associated with Coagulation and Inflammation: A Retrospective Cross-Sectional Study

Yu,

Guo,

Gong

2023

IDR

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Introduction Tuberculosis (TB) is a life-threatening single infectious disease, which remains a major global public health concern. This study was to establish and validate a clinically practical diagnostic scoring system for predicting active pulmonary tuberculosis (APTB) in patients with positive tuberculosis T cell spot test [T-SPOT] using indicators associated with coagulation and inflammation. Methods A single-center retrospective cross-sectional study was performed to include patients with positive T-SOPT registered and hospitalized at Wuhan Jinyintan Hospital between January 2017 and December 2019. All patients were separated into the active pulmonary tuberculosis (APTB) group and the inactive pulmonary tuberculosis (IPTB) group, according to the diagnostic criteria from China’s Expert Consensus for APTB and IPTB. Subsequently, the patients were randomized into a training set and a validation set at a ratio of 2:1. Indicators associated with coagulation and inflammation, including prothrombin time activity (PTA), activated partial thromboplastin time (APTT), thrombin time (TT), fibrinogen concentration (Fbg-C), C-reactive protein/albumin ratio (CAR), C-reactive protein/prealbumin ratio (CPR), neutrophils count/lymphocyte count ratio (NLR), platelet count/lymphocyte count ratio (PLR), monocyte count/lymphocyte count ratio (MLR), and erythrocyte sedimentation rate (ESR) were obtained from electronic medical record system (EMRS). Stepwise logistic regression was performed in the training set to build a diagnostic model for predicting APTB, which was transformed into an easily applicable scoring system via nomogram. Receiver operating characteristic (ROC) analysis, calibration curve (CC), and decision curve analysis (DCA) were conducted to evaluate the predictive performance of the established diagnostic scoring system. Results A total of 508 patients [training set (211 cases of APTB and 116 cases of IPTB) and validation set (103 cases of APTB and 78 cases of IPTB)] with positive T-SPOT were recruited in the study. Stepwise logistic regression showed that CPR, MLR, ESR, APTT and Fbg-C were independent predictors for APTB. The scoring system was subsequently formulated based on the abovementioned predictors, which correspond to scores of 10, 6, 7, 5, and 5, respectively. In addition, patients are more likely to be diagnosed as APTB when the cut-off score was ≥16 scores, while patients with <16 scores are more likely to be diagnosed as IPTB. The scoring system showed good predictive efficacy in both the training set [area under the curve (AUC): 0.887] and the validation set (AUC: 0.898). Furthermore, both CC and DCA confirmed the clinical utility of the scoring system. Conclusion The data suggest that the combination of indicators associated with coagulation and inflammation could serve as biomarkers to identify APTB in patients with positive T-SPOT. In addition, patients with positive T-SPOT were more prone ...

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“…In addition, approaches that could combine this information to generate a predictive algorithm for active TB were considered [21]. Algorithms such as a clinical score [22] developed using regression models or an artificial neural network (ANN) [23, 24] were identified. For these approaches, some computation would be necessary by the diagnosing health professional using a scorecard where points are allocated to individual or combinations of characteristics.…”

Section: Methodsmentioning

confidence: 99%

Modelling the impact of chest X-ray and alternative triage approaches prior to seeking a tuberculosis diagnosis

et al. 2019

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BackgroundTuberculosis is a major challenge to health in the developing world. Triage prior to diagnostic testing could potentially reduce the volume of tests and costs associated with using the more accurate, but costly, Xpert MTB/RIF assay. An effective methodology to predict the impact of introducing triage prior to tuberculosis diagnostic testing could be useful in helping to guide policy.MethodsThe development and use of operational modelling to project the impact on case detection and health system costs of alternative triage approaches for tuberculosis, with or without X-ray, based on data from Porto Alegre City, Brazil.ResultsMost of the triage approaches modelled without X-ray were predicted to provide no significant benefit. One approach based on an artificial neural network applied to patient and symptom characteristics was projected to increase case detection (82% vs. 75%) compared to microscopy, and reduce costs compared to Xpert without triage. In addition, use of X-ray before diagnostic testing for HIV-negative patients could maintain diagnostic yield of using Xpert without triage, and reduce costs.ConclusionA model for the impact assessment of alternative triage approaches has been tested. The results from using the approach demonstrate its usefulness in informing policy in a typical high burden setting for tuberculosis.Electronic supplementary materialThe online version of this article (10.1186/s12879-019-3684-1) contains supplementary material, which is available to authorized users.

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

Abstract: 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.

Cited by 18 publications

References 29 publications

Combining metabolome and clinical indicators with machine learning provides some promising diagnostic markers to precisely detect smear-positive/negative pulmonary tuberculosis

Combining metabolome and clinical indicators with machine learning provides some promising diagnostic markers to precisely detect smear-positive/negative pulmonary tuberculosis

Construction and Validation of a Diagnostic Scoring System for Predicting Active Pulmonary Tuberculosis in Patients with Positive T-SPOT Based on Indicators Associated with Coagulation and Inflammation: A Retrospective Cross-Sectional Study

Modelling the impact of chest X-ray and alternative triage approaches prior to seeking a tuberculosis diagnosis

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