ArticleFrontiers in molecular biosciences2024
Breathomics for diagnosing tuberculosis in diabetes mellitus patients.
Article in Frontiers in molecular biosciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- AI Based Cough Analysis for Pulmonary Tuberculosis Triage and Diagnosis: A Technical Review.npj biomedical innovations · 2026Review
- Investigation of sputum volatiles to classify active tuberculosis: A pilot study.Tuberculosis (Edinburgh, Scotland) · 2026Article
- Advanced Technologies for the Diagnosis of Pulmonary Tuberculosis Using Exhaled Breath Samples: A Systematic Scoping Review.Tropical medicine & international health : TM & IH · 2026Article
- Gut microbiota characteristics and its biological function analysis in patients with tuberculosis and diabetes mellitus co-morbidity.BMC microbiology · 2026Article
- The diagnostic value of CXCL8 in diabetes mellitus co-morbidity with tuberculosis.BMC infectious diseases · 2026Article
- Integrated multi-omics profiling reveals immune-related biomarkers and regulatory networks for early prediction of tuberculosis in type 2 diabetes mellitus.Frontiers in immunology · 2026Article
- Development and validation of a nomogram for predicting unfavorable treatment outcomes in patients with pulmonary tuberculosis and diabetes mellitus.Frontiers in medicine · 2026Article
- Analysis of High-Risk Factors for Tuberculosis Retreatment Based on Machine Learning and Latent Class Analysis.Infection and drug resistance · 2026Article
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11 authors.
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Abstract
Introduction: Individuals with diabetes mellitus (DM) are at an increased risk of Methods: Exhaled breath samples were collected and analyzed using high-pressure photon ionization time-of-flight mass spectrometry. An eXtreme Gradient Boosting (XGBoost) model was utilized for breathomics analysis and TB detection. Results: XGBoost model achieved a sensitivity of 88.5%, specificity of 100%, accuracy of 90.2%, and an area under the curve (AUC) of 98.8%. The most significant feature across the entire set was m106, which demonstrated a sensitivity of 93%, specificity of 100%, and an AUC of 99.7%. Discussion: The breathomics-based TB detection method utilizing m106 exhibited high sensitivity and specificity potentially beneficial for clinical TB screening and diagnosis in individuals with diabetes.
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