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

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  • 1Medicine, Division of Respirology, University of Toronto, Toronto, Ontario, Canada.

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A new deep learning model enhances spirometry interpretation, improving lung disease diagnosis. This AI tool achieves high accuracy, outperforming pulmonologists in classifying lung physiology using only spirometry data.

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Area of Science:

  • Pulmonary Medicine
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Spirometry and plethysmography are standard pulmonary function tests (PFTs) for lung disease.
  • Plethysmography's inaccessibility often leads to spirometry being used alone, risking missed or misdiagnoses, particularly for restrictive lung diseases.
  • There is a need for improved interpretation of spirometry alone.

Purpose of the Study:

  • To develop a deep learning model for enhanced interpretation of spirometry.
  • To improve the diagnostic accuracy of spirometry without the need for plethysmography.

Main Methods:

  • A multilayer perceptron deep learning model was developed using full PFT data from multiple patient cohorts.
  • Inputs included spirometry, plethysmography, and biometric data.
  • The model was trained, validated, and tested on independent datasets and compared against decision tree and pulmonologist interpretations.

Main Results:

  • The deep learning model using biometrics and spirometry achieved 95% accuracy after refinement.
  • The final model significantly outperformed decision tree (75.61%) and pulmonologist (66.67%) interpretations.
  • The model's accuracy was comparable to using full PFTs including plethysmography.

Conclusions:

  • Deep learning significantly enhances the diagnostic capability of spirometry.
  • The model accurately classifies lung physiology, achieving performance comparable to comprehensive PFTs.
  • This AI-driven approach offers a promising solution for improving lung disease diagnosis where plethysmography is unavailable.