Detection for New Biomarkers of Tuberculosis Infection Activity Using Machine Learning Methods

TL;DR

This paper reviews how machine learning and omics data can improve the detection of active tuberculosis by identifying new biomarkers that distinguish it from latent infection.

Contribution

The paper systematically compares ML-based approaches and identifies translational barriers in TB biomarker research.

Findings

ML-driven analyses outperform traditional tests in diagnosing tuberculosis.

Multimodal integration improves diagnostic accuracy and robustness.

qRT-PCR-based biomarker panels show promise for clinical use.

Abstract

Background/Objectives: Latent tuberculosis infection (LTBI) represents a critical reservoir for subsequent development of active tuberculosis (ATB) and poses significant challenges for early diagnosis and disease prevention. Traditional immunological assays, such as interferon-gamma release assays (IGRAs), are limited in their ability to reliably distinguish LTBI from ATB. Recent advances in high-throughput omics technologies and machine learning (ML) approaches offer new opportunities for precise, biomarker-based differential diagnostics. Methods: Transcriptomic and proteomic profiling of host immune responses has revealed reproducible gene and protein signatures associated with LTBI and ATB. The integration of ML techniques—including feature selection, dimensionality reduction, multimodal learning, and explainable AI—facilitates the construction of robust diagnostic models. Single-modality signatures, derived from RNA-seq, microarrays, or proteomic assays, are complemented by multimodal approaches that incorporate soluble mediators, immunological readouts, and imaging-derived features. Deep learning frameworks, such as convolutional neural networks and transformer-based architectures, enhance the extraction of complex molecular and structural patterns from high-dimensional datasets. Results: ML-driven analyses of transcriptomic and proteomic data consistently outperform conventional immunological tests in terms of sensitivity, specificity, and clinical applicability. Multimodal integration further improves diagnostic accuracy and robustness. These advances support the translational development of concise, quantitative reverse transcription PCR (qRT-PCR)-based biomarker panels suitable for routine clinical application, enabling early and reliable differentiation between LTBI and ATB. Overall, the combination of high-throughput omics and AI-based analytical frameworks provides a promising pathway for enhancing global tuberculosis diagnostics. Conclusions: This review provides a structured and critical synthesis of transcriptomic and proteomic biomarker research for LTBI and ATB discrimination, with a particular emphasis on machine learning–based analytical frameworks. Unlike previous narrative reviews, we systematically compare data-generating platforms, modelling strategies, validation approaches, and sources of heterogeneity across studies. We further identify key translational barriers, including cohort homogeneity, platform dependency, and limited external validation, and propose directions for future research aimed at improving clinical applicability.

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