ArticleBMC infectious diseases2022
Development of diagnostic algorithm using machine learning for distinguishing between active tuberculosis and latent tuberculosis infection.
Article in BMC infectious diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- Diagnostic performance of biomarkers for differentiating active tuberculosis from latent tuberculosis: a systematic review and Bayesian network meta-analysis.Frontiers in microbiology · 2024Pooled it
- Artificial Intelligence in Infectious Disease Care: Selected Applications in Tuberculosis, Sepsis, and Antimicrobial Stewardship.Diagnostics (Basel, Switzerland) · 2026Review
- Machine-Learning-Derived, Mechanistically Informed Transcriptomic Signature to Diagnose Active Tuberculosis and Guide Host-Directed Therapy.Diagnostics (Basel, Switzerland) · 2026Article
- Detection for New Biomarkers of Tuberculosis Infection Activity Using Machine Learning Methods.Diseases (Basel, Switzerland) · 2026Review
- Clinical application of chest CT image screening combined with nanopore targeted sequencing in improving the diagnosis rate of pulmonary tuberculosis patients.Frontiers in public health · 2026Observational
- Development of a machine learning model for early pulmonary tuberculosis diagnosis using blood test biomarkers.BMC infectious diseases · 2025Article
- Gut microbiota and tuberculosis.iMeta · 2025Review
- Artificial intelligence for tuberculosis control: a scoping review of applications in public health.Journal of global health · 2025Article
- Machine learning of immune-related laboratory indicators enables discrimination between pulmonary and extrapulmonary tuberculosis.Frontiers in medicine · 2025Article
- The role of ESAT-6 in tuberculosis immunopathology.Frontiers in immunology · 2024Review
- From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning.Military Medical Research · 2023Review
- Using an Artificial Intelligence Approach to Predict the Adverse Effects and Prognosis of Tuberculosis.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundThe discrimination between active tuberculosis (ATB) and latent tuberculosis infection (LTBI) remains challenging. The present study aims to investigate the value of diagnostic models established by machine learning based on multiple laboratory data for distinguishing Mycobacterium tuberculosis (Mtb) infection status.
methodsT-SPOT, lymphocyte characteristic detection, and routine laboratory tests were performed on participants. Diagnostic models were built according to various algorithms.
resultsA total of 892 participants (468 ATB and 424 LTBI) and another 263 participants (125 ATB and 138 LTBI), were respectively enrolled at Tongji Hospital (discovery cohort) and Sino-French New City Hospital (validation cohort). Receiver operating characteristic (ROC) curve analysis showed that the value of individual indicator for differentiating ATB from LTBI was limited (area under the ROC curve (AUC) < 0.8). A total of 28 models were successfully established using machine learning. Among them, the AUCs of 25 models were more than 0.9 in test set. It was found that conditional random forests (cforest) model, based on the implementation of the random forest and bagging ensemble algorithms utilizing conditional inference trees as base learners, presented best discriminative power in segregating ATB from LTBI. Specially, cforest model presented an AUC of 0.978, with the sensitivity of 93.39% and the specificity of 91.18%. Mtb-specific response represented by early secreted antigenic target 6 (ESAT-6) and culture filtrate protein 10 (CFP-10) spot-forming cell (SFC) in T-SPOT assay, as well as global adaptive immunity assessed by CD4 cell IFN-γ secretion, CD8 cell IFN-γ secretion, and CD4 cell number, were found to contribute greatly to the cforest model. Superior performance obtained in the discovery cohort was further confirmed in the validation cohort. The sensitivity and specificity of cforest model in validation set were 92.80% and 89.86%, respectively.
conclusionsCforest model developed upon machine learning could serve as a valuable and prospective tool for identifying Mtb infection status. The present study provided a novel and viable idea for realizing the clinical diagnostic application of the combination of machine learning and laboratory findings.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.