Cargando…

Application of Machine Learning in Pulmonary Function Assessment Where Are We Now and Where Are We Going?

Analysis of pulmonary function tests (PFTs) is an area where machine learning (ML) may benefit clinicians, researchers, and the patients. PFT measures spirometry, lung volumes, and carbon monoxide diffusion capacity of the lung (DLCO). The results are usually interpreted by the clinicians using disc...

Descripción completa

Detalles Bibliográficos
Autores principales: Giri, Paresh C., Chowdhury, Anand M., Bedoya, Armando, Chen, Hengji, Lee, Hyun Suk, Lee, Patty, Henriquez, Craig, MacIntyre, Neil R., Huang, Yuh-Chin T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8264499/
https://www.ncbi.nlm.nih.gov/pubmed/34248665
http://dx.doi.org/10.3389/fphys.2021.678540
_version_ 1783719571027394560
author Giri, Paresh C.
Chowdhury, Anand M.
Bedoya, Armando
Chen, Hengji
Lee, Hyun Suk
Lee, Patty
Henriquez, Craig
MacIntyre, Neil R.
Huang, Yuh-Chin T.
author_facet Giri, Paresh C.
Chowdhury, Anand M.
Bedoya, Armando
Chen, Hengji
Lee, Hyun Suk
Lee, Patty
Henriquez, Craig
MacIntyre, Neil R.
Huang, Yuh-Chin T.
author_sort Giri, Paresh C.
collection PubMed
description Analysis of pulmonary function tests (PFTs) is an area where machine learning (ML) may benefit clinicians, researchers, and the patients. PFT measures spirometry, lung volumes, and carbon monoxide diffusion capacity of the lung (DLCO). The results are usually interpreted by the clinicians using discrete numeric data according to published guidelines. PFT interpretations by clinicians, however, are known to have inter-rater variability and the inaccuracy can impact patient care. This variability may be caused by unfamiliarity of the guidelines, lack of training, inadequate understanding of lung physiology, or simply mental lapses. A rules-based automated interpretation system can recapitulate expert’s pattern recognition capability and decrease errors. ML can also be used to analyze continuous data or the graphics, including the flow-volume loop, the DLCO and the nitrogen washout curves. These analyses can discover novel physiological biomarkers. In the era of wearables and telehealth, particularly with the COVID-19 pandemic restricting PFTs to be done in the clinical laboratories, ML can also be used to combine mobile spirometry results with an individual’s clinical profile to deliver precision medicine. There are, however, hurdles in the development and commercialization of the ML-assisted PFT interpretation programs, including the need for high quality representative data, the existence of different formats for data acquisition and sharing in PFT software by different vendors, and the need for collaboration amongst clinicians, biomedical engineers, and information technologists. Hurdles notwithstanding, the new developments would represent significant advances that could be the future of PFT, the oldest test still in use in clinical medicine.
format Online
Article
Text
id pubmed-8264499
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-82644992021-07-09 Application of Machine Learning in Pulmonary Function Assessment Where Are We Now and Where Are We Going? Giri, Paresh C. Chowdhury, Anand M. Bedoya, Armando Chen, Hengji Lee, Hyun Suk Lee, Patty Henriquez, Craig MacIntyre, Neil R. Huang, Yuh-Chin T. Front Physiol Physiology Analysis of pulmonary function tests (PFTs) is an area where machine learning (ML) may benefit clinicians, researchers, and the patients. PFT measures spirometry, lung volumes, and carbon monoxide diffusion capacity of the lung (DLCO). The results are usually interpreted by the clinicians using discrete numeric data according to published guidelines. PFT interpretations by clinicians, however, are known to have inter-rater variability and the inaccuracy can impact patient care. This variability may be caused by unfamiliarity of the guidelines, lack of training, inadequate understanding of lung physiology, or simply mental lapses. A rules-based automated interpretation system can recapitulate expert’s pattern recognition capability and decrease errors. ML can also be used to analyze continuous data or the graphics, including the flow-volume loop, the DLCO and the nitrogen washout curves. These analyses can discover novel physiological biomarkers. In the era of wearables and telehealth, particularly with the COVID-19 pandemic restricting PFTs to be done in the clinical laboratories, ML can also be used to combine mobile spirometry results with an individual’s clinical profile to deliver precision medicine. There are, however, hurdles in the development and commercialization of the ML-assisted PFT interpretation programs, including the need for high quality representative data, the existence of different formats for data acquisition and sharing in PFT software by different vendors, and the need for collaboration amongst clinicians, biomedical engineers, and information technologists. Hurdles notwithstanding, the new developments would represent significant advances that could be the future of PFT, the oldest test still in use in clinical medicine. Frontiers Media S.A. 2021-06-24 /pmc/articles/PMC8264499/ /pubmed/34248665 http://dx.doi.org/10.3389/fphys.2021.678540 Text en Copyright © 2021 Giri, Chowdhury, Bedoya, Chen, Lee, Lee, Henriquez, MacIntyre and Huang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Physiology
Giri, Paresh C.
Chowdhury, Anand M.
Bedoya, Armando
Chen, Hengji
Lee, Hyun Suk
Lee, Patty
Henriquez, Craig
MacIntyre, Neil R.
Huang, Yuh-Chin T.
Application of Machine Learning in Pulmonary Function Assessment Where Are We Now and Where Are We Going?
title Application of Machine Learning in Pulmonary Function Assessment Where Are We Now and Where Are We Going?
title_full Application of Machine Learning in Pulmonary Function Assessment Where Are We Now and Where Are We Going?
title_fullStr Application of Machine Learning in Pulmonary Function Assessment Where Are We Now and Where Are We Going?
title_full_unstemmed Application of Machine Learning in Pulmonary Function Assessment Where Are We Now and Where Are We Going?
title_short Application of Machine Learning in Pulmonary Function Assessment Where Are We Now and Where Are We Going?
title_sort application of machine learning in pulmonary function assessment where are we now and where are we going?
topic Physiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8264499/
https://www.ncbi.nlm.nih.gov/pubmed/34248665
http://dx.doi.org/10.3389/fphys.2021.678540
work_keys_str_mv AT giripareshc applicationofmachinelearninginpulmonaryfunctionassessmentwherearewenowandwherearewegoing
AT chowdhuryanandm applicationofmachinelearninginpulmonaryfunctionassessmentwherearewenowandwherearewegoing
AT bedoyaarmando applicationofmachinelearninginpulmonaryfunctionassessmentwherearewenowandwherearewegoing
AT chenhengji applicationofmachinelearninginpulmonaryfunctionassessmentwherearewenowandwherearewegoing
AT leehyunsuk applicationofmachinelearninginpulmonaryfunctionassessmentwherearewenowandwherearewegoing
AT leepatty applicationofmachinelearninginpulmonaryfunctionassessmentwherearewenowandwherearewegoing
AT henriquezcraig applicationofmachinelearninginpulmonaryfunctionassessmentwherearewenowandwherearewegoing
AT macintyreneilr applicationofmachinelearninginpulmonaryfunctionassessmentwherearewenowandwherearewegoing
AT huangyuhchint applicationofmachinelearninginpulmonaryfunctionassessmentwherearewenowandwherearewegoing