Preliminary investigation of human exhaled breath for tuberculosis diagnosis by multidimensional gas chromatography – Time of flight mass spectrometry and machine learning

Beccaria, Marco; Mellors, Theodore; Petion, Jacky S.; Rees, Christiaan A.; Nasir, Mavra; Systrom, Hannah; Sairistil, Jean W.; Jean-Juste, Marc-Antoine; Rivera, Vanessa; Lavoile, Kerline; Sévère, Patrice; Pape, Jean William; Wright, Peter F.; Hill, Jane E.

doi:10.1016/j.jchromb.2018.01.004

Cited by 53 publications

(54 citation statements)

References 29 publications

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“…35 Meanwhile, findings from studies that reported improvement in diagnosis sensitivity and specificity by applying machine learning to the multi-biomarker analysis have been used in real-world cases to classify the cancer-related biomarkers; moreover, machine learning has been applied in cases of in-vitro diagnosis of diseases such as tuberculosis and dementia. 36 37 38 39 …”

Section: Ai Application Areas In Health Carementioning

confidence: 99%

Artificial Intelligence in Health Care: Current Applications and Issues

et al. 2020

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In recent years, artificial intelligence (AI) technologies have greatly advanced and become a reality in many areas of our daily lives. In the health care field, numerous efforts are being made to implement the AI technology for practical medical treatments. With the rapid developments in machine learning algorithms and improvements in hardware performances, the AI technology is expected to play an important role in effectively analyzing and utilizing extensive amounts of health and medical data. However, the AI technology has various unique characteristics that are different from the existing health care technologies. Subsequently, there are a number of areas that need to be supplemented within the current health care system for the AI to be utilized more effectively and frequently in health care. In addition, the number of medical practitioners and public that accept AI in the health care is still low; moreover, there are various concerns regarding the safety and reliability of AI technology implementations. Therefore, this paper aims to introduce the current research and application status of AI technology in health care and discuss the issues that need to be resolved.

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Section: Ai Application Areas In Health Carementioning

confidence: 99%

Artificial Intelligence in Health Care: Current Applications and Issues

et al. 2020

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“…Based on such GC-MS approaches, a limited number of VOCs has been tentatively identified as part of a volatile lung cancer profile [19,31,43,[45][46][47][48][49][50]. However, because of the limited peak capacity and sensitivity of GC-MS, but also because of the lack of robustness, quality control, and validation of sampling and analysis, the approach has not yet found its way to clinical application [51]. Peak capacity and sensitivity can be enhanced by using comprehensive twodimensional gas chromatography coupled to time-of-flight MS (GC×GC-TOFMS), a known efficient separation technique for complex sample analysis [51][52][53].…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…However, because of the limited peak capacity and sensitivity of GC-MS, but also because of the lack of robustness, quality control, and validation of sampling and analysis, the approach has not yet found its way to clinical application [51]. Peak capacity and sensitivity can be enhanced by using comprehensive twodimensional gas chromatography coupled to time-of-flight MS (GC×GC-TOFMS), a known efficient separation technique for complex sample analysis [51][52][53]. Basically, GC×GC relies on the use of two different GC phases connected in series via a modulator [54] that, when using cryogens to operate, not only enhances the separation power but also provides better limits of detection by cryogenic zone compression of chromatographic peaks [55].…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Multimodal chemometric approach for the analysis of human exhaled breath in lung cancer patients by TD-GC × GC-TOFMS

Pesesse

Stefanuto

Schleich

et al. 2019

Journal of Chromatography B

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Lung cancer is the deadliest cancer in developed countries. To reduce its mortality rate, it is important to enhance our capability to detect it at earlier stages by developing early diagnostic methods. In that context, the analysis of exhaled breath is an interesting approach because of the simplicity of the medical act and its non-invasiveness. Thermal desorption comprehensive two-dimensional gas chromatography time of flight mass spectrometry (TD-GC×GC

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“…Numerous research teams had utilized gas chromatography (GC) interrelated to mass spectrometry (MS), for inspecting the volatile molecules detected in breath in cases with active pulmonary TB, and detected diverse forms of marker compounds [4] [5] [6] owing to different sampling methods and analytical approaches along with patient population heterogeneity and patient comorbidities [7].…”

Section: Introductionmentioning

confidence: 99%

Impact of Pulmonary Tuberculosis Activity on Exhaled Breath Markers Levels in the Egyptian Population

Abumossalam¹,

Ehab²,

El-Halaby³

et al. 2020

JTR

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Introduction: Tuberculosis still characterizes till now a major respiratory insult with concurrent pulmonary manifestations and later disability. Aim of Work: To evaluate the level of exhaled fraction of nitric oxide (FENO) and fraction of exhaled carbon monoxide (FECO) as markers of pulmonary tuberculosis TB activity in patients under chemotherapy in comparison to healthy negative patients and latent TB patients. Patients and Methods: This cross-sectional study was conducted on 130 patients recruited from the outpatient clinic of Mansoura Chest hospital during the period from May 2019 to December 2019. They were categorized into the three groups: 1) Pulmonary tuberculous patients PTB (group1) which included 48 cases with positive sputum for TB bacilli in the initiation phase after 1 month of starting anti-tuberculous chemotherapy; 2) Latent patients (group 2): included 40 patients with positive tuberculin skin test (Mantoux test) > 10 mm. 3) Control patients (group 3) which included 42 healthy volunteers with negative sputum for TB bacilli. They were subjected to portable spirogram as well as exhaled fractional NO and CO measurement. Results: FECO and FENO levels prevailed in pulmonary TB patients followed by Latent TB patients and lastly healthy volunteers (42 ± 12.32/5 ± 0.16 & 38 ± 8.25/6 ± 2.25 and 23 ± 3.25/2 ± 0.40 respectively). Conclusion: Measurement of CO and NO level in expired air may correlate with active pulmonary TB infection in comparison to healthy negative tuberculous patients and latent tuberculous patients.

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Preliminary investigation of human exhaled breath for tuberculosis diagnosis by multidimensional gas chromatography – Time of flight mass spectrometry and machine learning

Cited by 53 publications

References 29 publications

Artificial Intelligence in Health Care: Current Applications and Issues

Artificial Intelligence in Health Care: Current Applications and Issues

Multimodal chemometric approach for the analysis of human exhaled breath in lung cancer patients by TD-GC × GC-TOFMS

Impact of Pulmonary Tuberculosis Activity on Exhaled Breath Markers Levels in the Egyptian Population

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Preliminary investigation of human exhaled breath for tuberculosis diagnosis by multidimensional gas chromatography – Time of flight mass spectrometry and machine learning

Cited by 53 publications

References 29 publications

Artificial Intelligence in Health Care: Current Applications and Issues

Artificial Intelligence in Health Care: Current Applications and Issues

Multimodal chemometric approach for the analysis of human exhaled breath in lung cancer patients by TD-GC × GC-TOFMS

Impact of Pulmonary Tuberculosis Activity on Exhaled Breath Markers Levels in the Egyptian Population

Contact Info

Product

Resources

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Multimodal chemometric approach for the analysis of human exhaled breath in lung cancer patients by TD-GC × GC-TOFMS