Diagnosis of tuberculosis through breath test: A systematic review

Saktiawati, Antonia Morita Iswari; Putera, David Dwi; Setyawan, Althaf; Mahendradhata, Yodi; Werf, Tjip S. van der

doi:10.1016/j.ebiom.2019.07.056

Cited by 59 publications

(42 citation statements)

References 44 publications

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“…The purpose of the current survey was to detect and monitor the efficient method for airborne disease prediction. Patient inspirational efforts, penetration or video blacks, and patient positioning/rotation adequacy are the three critical technological considerations for chest X-rays [14,15]. This article explains chest X-rays in the test for Tuberculosis.…”

Section: Chest Radiography and Tuberculosis Screeningmentioning

confidence: 99%

“…The geometrical feature-learning course's principal aim is to find a collection of representative features of geometric form that reflect an entity by extracting geometrical features from images. In addition to solving recognition problems, geometrical function learning methods can also be predicted in the following steps that evaluate the sequential input sensory images [14].…”

Section: Geometry Featuresmentioning

confidence: 99%

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Analysis of Deep Learning Techniques for Tuberculosis Disease

2021

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The airborne disease is a severe disease in the world that spreads exponentially. An Immunochromatography Test(IT) and Biological Aerosol Particles (BAP) test with a disposable instrument that will be recorded in the literature is a standard diagnostic procedure for respiratory infections, such as influenza and TB, in the investigation causes of suspected disease. This examination helps the examiner to identify infectious patients quickly, efficiently, and inexpensively. Self-diagnosis, however, is problematic because it requires a medical specialist to diagnose the diseases in infections. Chest radiography is also not only the primary screening method that is focused on surgical imaging but also diagnostic radiology. Various methods of classification of Tuberculosis are analyzed here. Computer-Assisted Diagnosis (CAD) has become popular, and many researchers are involved in these fields of study. Numerous methods have been suggested for airborne identification and Classification of lung diseases. The medical history of airborne tuberculosis disease in chest X-rays and a review of the different approaches to diagnosing and Classification of the airborne disease are discussed in this article. According to the literature on the relevant policies, items in this research field have been surveyed until 2020.

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Section: Chest Radiography and Tuberculosis Screeningmentioning

confidence: 99%

Section: Geometry Featuresmentioning

confidence: 99%

Analysis of Deep Learning Techniques for Tuberculosis Disease

2021

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“…The Aeonose (eNose BV, Zutphen, Netherlands) constitutes an example of such device and it has been tested in the field [70]. Preliminary results have so far fared relatively poorly [71], however the portability and the rapid generation time of results (<5 min) makes the prospect of this technology highly appealing particularly, in rural areas.…”

Section: Microrna Detectionmentioning

confidence: 99%

Tuberculosis diagnostics: overcoming ancient challenges with modern solutions

Macgregor-Fairlie

Wilkinson

Besra

et al. 2020

Emerging Topics in Life Sciences

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Rapid, sensitive, accurate and portable diagnostics are a mainstay of modern medicine. Tuberculosis is a disease that has been with us since time immemorial and, despite the fact that it can be treated and cured, it still remains the world's biggest infectious killer, taking the lives of millions annually. There have been important developments in the diagnostic devices for tuberculosis however, these are often prone to error, expensive, lack the necessary sensitivity or accuracy and, crucially, not sufficiently portable and thus not applicable in the remote, rural areas, where they are most needed. Modern solutions have been emerging in the past decade, seeking to overcome many of the inhibiting issues in this field by utilising recent advances in molecular biology, genetics and sequencing or even completely ‘reinventing the wheel’, by developing novel and unprecedented diagnostic techniques. In this mini review, the issues and challenges arising from the historical methods of diagnosing tuberculosis are discussed, followed by outlaying their particular lack of appropriateness for regions of the world where tuberculosis still remains endemic. Subsequently, more recent developments of new methods and technological advancements as ‘modern weapons’ in the battle to defeat this disease and associated challenges are reviewed, and finally an outlook is presented, highlighting the future of the modern solutions under development, which are envisioned to lay the platform for improvements in delivering timely intervention, reduce immense expense and burden on healthcare systems worldwide, while saving millions of lives and eventually, may enable the eradication of this ancient disease.

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“…The outbreak of the novel corona virus SARS‐CoV‐2 (COVID‐19) originated in Wuhan China during December 2019 and has spread globally, resulting with ∼70 million infections and 1.5 million deaths to date and rising. [ 1 , 2 ] As of now, it is clear that early‐stage diagnosis of COVID‐19, as well as other pandemic‐related diseases, leads to higher survival rates and prevention of the spread of disease. [ 3 ] So far, the diagnosis of COVID‐19 follows molecular testing for the presence of the disease.…”

Section: Introductionmentioning

confidence: 99%

Biodiagnostics in an era of global pandemics—From biosensing materials to data management

Broza

Haick

2021

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The novel corona virus SARS‐CoV‐2 (COVID‐19) has exposed the world to challenges never before seen in fast diagnostics, monitoring, and prevention of the outbreak. As a result, different approaches for fast diagnostic and screening are made and yet to find the ideal way. The current mini‐review provides and examines evidence‐based innovative and rapid chemical sensing and related biodiagnostic solutions to deal with infectious disease and related pandemic emergencies, which could offer the best possible care for the general population and improve the approachability of the pandemic information, insights, and surrounding contexts. The review discusses how integration of sensing devices with big data analysis, artificial Intelligence or machine learning, and clinical decision support system, could improve the accuracy of the recorded patterns of the disease conditions within an ocean of information. At the end, the mini‐review provides a prospective on the requirements to improve our coping of the pandemic‐related biodiagnostics as well as future opportunities.

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Diagnosis of tuberculosis through breath test: A systematic review

Cited by 59 publications

References 44 publications

Analysis of Deep Learning Techniques for Tuberculosis Disease

Analysis of Deep Learning Techniques for Tuberculosis Disease

Tuberculosis diagnostics: overcoming ancient challenges with modern solutions

Biodiagnostics in an era of global pandemics—From biosensing materials to data management

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