ArticleScientific reports2022
A pilot study for the prediction of liver function related scores using breath biomarkers and machine learning.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 20 citations in OpenAlex.
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- Systematic comparison of methods for offline breath sampling.Analytical and bioanalytical chemistry · 2025Article
- Sensitive Detection of Specific Volatile Organic Compounds by Functionalized Transition Metal Dichalcogenide Monolayers.Langmuir : the ACS journal of surfaces and colloids · 2025Article
- Steps toward clinical validation of exhaled volatile organic compound biomarkers for hypoglycemia in persons with type 1 diabetes.Scientific reports · 2025Article
- Harnessing serum VOCs and machine learning for the early detection of MAFLD.Frontiers in endocrinology · 2025Article
- Progress and challenges of developing volatile metabolites from exhaled breath as a biomarker platform.Metabolomics : Official journal of the Metabolomic Society · 2024Review
- Machine learning insights concerning inflammatory and liver-related risk comorbidities in non-communicable and viral diseases.World journal of gastroenterology · 2022Review
- Demystifying Supervised Learning in Healthcare 4.0: A New Reality of Transforming Diagnostic Medicine.Diagnostics (Basel, Switzerland) · 2022Review
- A deep learning approach for detecting liver cirrhosis from volatolomic analysis of exhaled breath.Frontiers in medicine · 2022Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Volatile organic compounds (VOCs) present in exhaled breath can help in analysing biochemical processes in the human body. Liver diseases can be traced using VOCs as biomarkers for physiological and pathophysiological conditions. In this work, we propose non-invasive and quick breath monitoring approach for early detection and progress monitoring of liver diseases using Isoprene, Limonene, and Dimethyl sulphide (DMS) as potential biomarkers. A pilot study is performed to design a dataset that includes the biomarkers concentration analysed from the breath sample before and after study subjects performed an exercise. A machine learning approach is applied for the prediction of scores for liver function diagnosis. Four regression methods are performed to predict the clinical scores using breath biomarkers data as features set by the machine learning techniques. A significant difference was observed for isoprene concentration (p < 0.01) and for DMS concentration (p < 0.0001) between liver patients and healthy subject's breath sample. The R-square value between actual clinical score and predicted clinical score is found to be 0.78, 0.82, and 0.85 for CTP score, APRI score, and MELD score, respectively. Our results have shown a promising result with significant different breath profiles between liver patients and healthy volunteers. The use of machine learning for the prediction of scores is found very promising for use of breath biomarkers for liver function diagnosis.
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