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Related Concept Videos

Pulmonary Function Tests01:25

Pulmonary Function Tests

Pulmonary Function Tests (PFTs)
Pulmonary Function Tests are crucial diagnostic tools for assessing respiratory function, particularly in patients with chronic respiratory disorders. They comprehensively evaluate lung volumes, ventilatory function, breathing mechanics, diffusion, and gas exchange. These tests help diagnose pulmonary diseases and play a significant role in monitoring disease progression, evaluating disability, and assessing response to therapy.
PFTs involve using a spirometer, a...

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Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
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Deep learning approach for probabilistic pulmonary function estimation from chest X-ray and peak expiratory flow

Christoph Killing1, Maximilian Wekerle2, Jayne S Sutherland3

  • 1Institute of Infectious Diseases and Tropical Medicine, LMU University Hospital, LMU Munich, Munich, Germany. christoph.killing@med.uni-muenchen.de.

Communications Medicine
|June 9, 2026
PubMed
Summary

This study introduces a novel deep learning framework to estimate lung function from chest X-rays, improving accuracy by addressing anatomical variability and integrating probabilistic reasoning for reliable pulmonary function assessment.

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Area of Science:

  • Medical Imaging
  • Pulmonary Medicine
  • Machine Learning

Background:

  • Spirometry is the gold standard for pulmonary function assessment.
  • Deep learning models show promise in estimating lung function from chest X-rays (CXR).
  • Existing methods struggle with anatomical variability and reliable estimation near diagnostic thresholds.

Purpose of the Study:

  • To develop a probabilistic machine learning framework for estimating FEV1 and FVC from CXRs.
  • To enhance estimation reliability by addressing anatomical variability.
  • To improve the accuracy of lung function estimation in diverse patient populations.

Main Methods:

  • Developed a probabilistic machine learning framework using morphologically regularized CXRs and PEFR.
  • Estimated FEV1 and FVC z-scores to decouple appearance from anatomical variability.
  • Validated the method on a multi-national cohort of pulmonary tuberculosis patients.

Main Results:

  • Achieved AUC of 0.879 for FEV1 and 0.853 for FVC in identifying moderate/severe impairment.
  • Demonstrated AUC improvement of 0.144 (FEV1) and 0.118 (FVC) over previous methods.
  • Further AUC rise to 0.894 (FEV1) and 0.857 (FVC) when allowing uncertainty, showing robust performance across pathologies.

Conclusions:

  • Decoupling anatomical variability from functional assessment enhances lung function estimation.
  • Probabilistic modeling improves diagnostic reliability near decision thresholds.
  • The system offers a practical approach for lung function estimation where spirometry is unavailable.