A pilot study for the prediction of liver function related scores using breath biomarkers and machine learning.

Autor: Patnaik RK; Institute of NanoEngineering and MicroSystems, National Tsing Hua University, Hsinchu, 30013, Taiwan., Lin YC; Institute of NanoEngineering and MicroSystems, National Tsing Hua University, Hsinchu, 30013, Taiwan., Agarwal A; Institute of NanoEngineering and MicroSystems, National Tsing Hua University, Hsinchu, 30013, Taiwan., Ho MC; Department of Surgery, National Taiwan University Hospital and College of Medicine, Taipei City, 100225, Taiwan. mcho1215@ntu.edu.tw.; Department of Surgery, National Taiwan University Hospital Hsin-Chu Branch, Zhubei City, Hsinchu County, 100225, Taiwan. mcho1215@ntu.edu.tw., Yeh JA; Institute of NanoEngineering and MicroSystems, National Tsing Hua University, Hsinchu, 30013, Taiwan. jayeh@mx.nthu.edu.tw.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2022 Feb 07; Vol. 12 (1), pp. 2032. Date of Electronic Publication: 2022 Feb 07.
DOI: 10.1038/s41598-022-05808-5
Abstrakt: 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.
(© 2022. The Author(s).)
Databáze: MEDLINE
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