Deep learning using multilayer perception improves the diagnostic acumen of spirometry: a single-centre Canadian study.

Journal: BMJ open respiratory research
PMID: 36572484

Abstract

RATIONALE: Spirometry and plethysmography are the gold standard pulmonary function tests (PFT) for diagnosis and management of lung disease. Due to the inaccessibility of plethysmography, spirometry is often used alone but this leads to missed or misdiagnoses as spirometry cannot identify restrictive disease without plethysmography. We aimed to develop a deep learning model to improve interpretation of spirometry alone.

Authors

  • Amanda Mac
    Medicine, Division of Respirology, University of Toronto, Toronto, Ontario, Canada.
  • Tong Xu
    Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Joyce K Y Wu
    Medicine, Division of Respirology, University of Toronto, Toronto, Ontario, Canada.
  • Natalia Belousova
    Medicine, Division of Respirology, University of Toronto, Toronto, Ontario, Canada.
  • Haruna Kitazawa
    Medicine, Division of Respirology, University of Toronto, Toronto, Ontario, Canada.
  • Nick Vozoris
    Medicine, Division of Respirology, University of Toronto, Toronto, Ontario, Canada.
  • Dmitry Rozenberg
    Medicine, Division of Respirology, University of Toronto, Toronto, Ontario, Canada.
  • Clodagh M Ryan
    Medicine, Division of Respirology, University of Toronto, Toronto, Ontario, Canada.
  • Shahrokh Valaee
  • Chung-Wai Chow
    Medicine, Division of Respirology, University of Toronto, Toronto, Ontario, Canada Chung-Wai.Chow@uhn.ca.

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