Machine learning approaches in diagnosing tuberculosis through biomarkers - A systematic review

Balakrishnan, Vimala; Kehrabi, Yousra; Ramanathan, Ghayathri; Paul, Scott Arjay; Tiong, Chiong Kian

doi:10.1016/j.pbiomolbio.2023.03.001

Cited by 10 publications

(2 citation statements)

References 42 publications

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“…Therefore, medical imaging data must be combined with other data sources for differential diagnosis. Biomarker data is the most important source for using ML methods to diagnose LTBI [ 282 ]. Previous research has found significant differences between individuals with LTBI and individuals with ATB or healthy individuals in various biomarkers [ 13 , 67 , 283 , 284 ].…”

Section: Future Directions Of ML For the Differential Diagnosis Of Ltbimentioning

confidence: 99%

From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning

Li,

Yang,

Zhuang

et al. 2023

Military Med Res

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Latent tuberculosis infection (LTBI) has become a major source of active tuberculosis (ATB). Although the tuberculin skin test and interferon-gamma release assay can be used to diagnose LTBI, these methods can only differentiate infected individuals from healthy ones but cannot discriminate between LTBI and ATB. Thus, the diagnosis of LTBI faces many challenges, such as the lack of effective biomarkers from Mycobacterium tuberculosis (MTB) for distinguishing LTBI, the low diagnostic efficacy of biomarkers derived from the human host, and the absence of a gold standard to differentiate between LTBI and ATB. Sputum culture, as the gold standard for diagnosing tuberculosis, is time-consuming and cannot distinguish between ATB and LTBI. In this article, we review the pathogenesis of MTB and the immune mechanisms of the host in LTBI, including the innate and adaptive immune responses, multiple immune evasion mechanisms of MTB, and epigenetic regulation. Based on this knowledge, we summarize the current status and challenges in diagnosing LTBI and present the application of machine learning (ML) in LTBI diagnosis, as well as the advantages and limitations of ML in this context. Finally, we discuss the future development directions of ML applied to LTBI diagnosis.

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Section: Future Directions Of ML For the Differential Diagnosis Of Ltbimentioning

confidence: 99%

From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning

Li,

Yang,

Zhuang

et al. 2023

Military Med Res

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“…To address the low-quality Raman spectra, random forests have a higher tolerance for noise over other algorithms. , Random forest is an ensemble learning model that uses bagging and random feature selection to construct multiple independent decision tree structures and then integrates the voting results of each decision tree for prediction . The ability of random forests to handle noisy and high-dimensional data, achieve high accuracy, and provide interpretable results makes it a valuable tool for scientific research in many fields . In this work, we applied a machine learning approach in automatic processing of Raman spectra to identify nanoplastics.…”

Section: Introductionmentioning

confidence: 99%

Automatic Identification of Individual Nanoplastics by Raman Spectroscopy Based on Machine Learning

Xie

Luo

Liu

et al. 2023

Environ. Sci. Technol.

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The increasing prevalence of nanoplastics in the environment underscores the need for effective detection and monitoring techniques. Current methods mainly focus on microplastics, while accurate identification of nanoplastics is challenging due to their small size and complex composition. In this work, we combined highly reflective substrates and machine learning to accurately identify nanoplastics using Raman spectroscopy. Our approach established Raman spectroscopy data sets of nanoplastics, incorporated peak extraction and retention data processing, and constructed a random forest model that achieved an average accuracy of 98.8% in identifying nanoplastics. We validated our method with tap water spiked samples, achieving over 97% identification accuracy, and demonstrated the applicability of our algorithm to real-world environmental samples through experiments on rainwater, detecting nanoscale polystyrene (PS) and polyvinyl chloride (PVC). Despite the challenges of processing lowquality nanoplastic Raman spectra and complex environmental samples, our study demonstrated the potential of using random forests to identify and distinguish nanoplastics from other environmental particles. Our results suggest that the combination of Raman spectroscopy and machine learning holds promise for developing effective nanoplastic particle detection and monitoring strategies.

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Innovations in Tuberculosis Disease Screening

Yousif,

Mesilhy,

Aly

et al. 2024

Surveillance, Prevention, and Control of Infectious Diseases

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Machine learning approaches in diagnosing tuberculosis through biomarkers - A systematic review

Cited by 10 publications

References 42 publications

From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning

From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning

Automatic Identification of Individual Nanoplastics by Raman Spectroscopy Based on Machine Learning

Innovations in Tuberculosis Disease Screening

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