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

Publication ,  Journal Article
Giri, PC; Chowdhury, AM; Bedoya, A; Chen, H; Lee, HS; Lee, P; Henriquez, C; MacIntyre, NR; Huang, Y-CT
Published in: Front Physiol
2021

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.

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Published In

Front Physiol

DOI

ISSN

1664-042X

Publication Date

2021

Volume

12

Start / End Page

678540

Location

Switzerland

Related Subject Headings

  • 3208 Medical physiology
  • 3101 Biochemistry and cell biology
  • 1701 Psychology
  • 1116 Medical Physiology
  • 0606 Physiology
 

Citation

APA
Chicago
ICMJE
MLA
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Giri, P. C., Chowdhury, A. M., Bedoya, A., Chen, H., Lee, H. S., Lee, P., … Huang, Y.-C. (2021). Application of Machine Learning in Pulmonary Function Assessment Where Are We Now and Where Are We Going? Front Physiol, 12, 678540. https://doi.org/10.3389/fphys.2021.678540
Giri, Paresh C., Anand M. Chowdhury, Armando Bedoya, Hengji Chen, Hyun Suk Lee, Patty Lee, Craig Henriquez, Neil R. MacIntyre, and Yuh-Chin T. Huang. “Application of Machine Learning in Pulmonary Function Assessment Where Are We Now and Where Are We Going?Front Physiol 12 (2021): 678540. https://doi.org/10.3389/fphys.2021.678540.
Giri PC, Chowdhury AM, Bedoya A, Chen H, Lee HS, Lee P, et al. Application of Machine Learning in Pulmonary Function Assessment Where Are We Now and Where Are We Going? Front Physiol. 2021;12:678540.
Giri, Paresh C., et al. “Application of Machine Learning in Pulmonary Function Assessment Where Are We Now and Where Are We Going?Front Physiol, vol. 12, 2021, p. 678540. Pubmed, doi:10.3389/fphys.2021.678540.
Giri PC, Chowdhury AM, Bedoya A, Chen H, Lee HS, Lee P, Henriquez C, MacIntyre NR, Huang Y-CT. Application of Machine Learning in Pulmonary Function Assessment Where Are We Now and Where Are We Going? Front Physiol. 2021;12:678540.

Published In

Front Physiol

DOI

ISSN

1664-042X

Publication Date

2021

Volume

12

Start / End Page

678540

Location

Switzerland

Related Subject Headings

  • 3208 Medical physiology
  • 3101 Biochemistry and cell biology
  • 1701 Psychology
  • 1116 Medical Physiology
  • 0606 Physiology