Y. Kistenev, A. Borisov, V. Nikolaev, D. Vrazhnov, D. А. Kuzmin
{"title":"Laser photoacoustic spectroscopy applications in breathomics","authors":"Y. Kistenev, A. Borisov, V. Nikolaev, D. Vrazhnov, D. А. Kuzmin","doi":"10.18287/JBPE19.05.010303","DOIUrl":null,"url":null,"abstract":"The breathomics approach to express-diagnosis of bronchopulmonary diseases based on spectral analysis of volatile organic compounds in a patient’s exhaled air is discussed. The basic demands and possible technical solutions to laser photoacoustic spectroscopy equipment in a framework of breathomics are presented. An example of differential diagnostics of the set of bronchopulmonary diseases, including lung cancer (LC) patients (N = 9); patients with chronic obstructive pulmonary disease (COPD) (N = 12); patients with pneumonia (N = 11) and a control group of healthy volunteers using breath air analysis by laser photoacoustic spectroscopy and machine learning is presented.","PeriodicalId":52398,"journal":{"name":"Journal of Biomedical Photonics and Engineering","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Biomedical Photonics and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18287/JBPE19.05.010303","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Physics and Astronomy","Score":null,"Total":0}
引用次数: 8
Abstract
The breathomics approach to express-diagnosis of bronchopulmonary diseases based on spectral analysis of volatile organic compounds in a patient’s exhaled air is discussed. The basic demands and possible technical solutions to laser photoacoustic spectroscopy equipment in a framework of breathomics are presented. An example of differential diagnostics of the set of bronchopulmonary diseases, including lung cancer (LC) patients (N = 9); patients with chronic obstructive pulmonary disease (COPD) (N = 12); patients with pneumonia (N = 11) and a control group of healthy volunteers using breath air analysis by laser photoacoustic spectroscopy and machine learning is presented.