Diagnosis and Severity Assessment of COPD Using a Novel Fast-Response Capnometer and Interpretable Machine Learning.

Journal: COPD
PMID: 38655897

Abstract

INTRODUCTION: Spirometry is the gold standard for COPD diagnosis and severity determination, but is technique-dependent, nonspecific, and requires administration by a trained healthcare professional. There is a need for a fast, reliable, and precise alternative diagnostic test. This study's aim was to use interpretable machine learning to diagnose COPD and assess severity using 75-second carbon dioxide (CO) breath records captured with TidalSense's N-Tidal capnometer.

Authors

  • Leeran Talker
    Department of Machine Learning, TidalSense, Cambridge, UK.
  • Cihan Dogan
    Department of Machine Learning, TidalSense, Cambridge, UK.
  • Daniel Neville
    Respiratory Department, Portsmouth Hospitals University NHS Foundation Trust, Portsmouth, UK.
  • Rui Hen Lim
    Department of Machine Learning, TidalSense, Cambridge, UK.
  • Henry Broomfield
    Department of Machine Learning, TidalSense, Cambridge, UK.
  • Gabriel Lambert
    Department of Clinical Operations, TidalSense, Cambridge, UK.
  • Ahmed Selim
    Department of Machine Learning, TidalSense, Cambridge, UK.
  • Thomas Brown
    Syapse Inc, San Francisco, CA.
  • Laura Wiffen
    Respiratory Department, Portsmouth Hospitals University NHS Foundation Trust, Portsmouth, UK.
  • Julian Carter
    Department of Engineering, TidalSense, Cambridge, UK.
  • Helen F Ashdown
    Department of Primary Care Health Sciences, NIHR Community Healthcare MedTech and IVD Cooperative, University of Oxford, Oxford, UK.
  • Gail Hayward
    Department of Primary Care Health Sciences, NIHR Community Healthcare MedTech and IVD Cooperative, University of Oxford, Oxford, UK.
  • Elango Vijaykumar
    Department of Research, Modality GP Partnership, UK.
  • Scott T Weiss
    From Research Information Systems and Computing (V.M.C., V.G., S.M.), Partners Healthcare; Boston Children's Hospital Informatics Program (D.D., S.F., G.S.); Harvard Medical School (D.D., S.Y., A.C., M.A.-E.-B., N.A.S., S.M., S.T.W., R.D.); Department of Medicine (S.Y., S.T.W.), Department of Neurosurgery (A.C., M.A.-E.-B., R.D.), Division of Rheumatology, Immunology and Allergy (N.A.S.), and Channing Division of Network Medicine (S.T.W., R.D.), Brigham and Women's Hospital, Boston, MA; Center for Statistical Science (S.Y.), Tsinghua University, Beijing, China; Department of Neurology (S.M.), Massachusetts General Hospital; and Biostatistics (T.C.), Harvard School of Public Health, Boston, MA.
  • Anoop Chauhan
    Respiratory Department, Portsmouth Hospitals University NHS Foundation Trust, Portsmouth, UK.
  • Ameera X Patel
    Executive Department, TidalSense, Cambridge, UK.

Keywords

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