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Artificial Intelligence-Based Emphysema Quantification in Routine Chest Computed Tomography: Correlation With

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This summary is machine-generated.

Artificial intelligence (AI)-based low attenuation volume percentage (LAV%) strongly correlates with lung function (FEV1/FVC) and emphysema severity on CT scans. This AI measurement shows promise for evaluating emphysema in routine chest CT imaging.

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Area of Science:

  • Pulmonary Medicine
  • Radiology
  • Artificial Intelligence in Medical Imaging

Background:

  • Chronic obstructive pulmonary disease (COPD) is a major health concern characterized by airflow limitation and emphysema.
  • Accurate assessment of emphysema severity and its correlation with lung function is crucial for patient management.
  • Routine chest computed tomography (CT) is widely used, but quantitative analysis of emphysema is evolving.

Purpose of the Study:

  • To evaluate the correlation between AI-derived low attenuation volume percentage (LAV%) and spirometric measures (FEV1/FVC).
  • To assess the relationship between AI-based LAV% and visually determined emphysema grades on chest CT.
  • To determine optimal LAV% cutoff values for predicting airflow obstruction and moderate-to-severe emphysema.

Main Methods:

  • Retrospective analysis of 298 patients with routine chest CT and spirometry.
  • Quantification of LAV% using AI software (threshold < -950 HU).
  • Visual grading of emphysema by a radiologist; calculation of Spearman correlation and ROC curves for LAV% and FEV1/FVC.

Main Results:

  • A significant negative correlation was found between LAV% and FEV1/FVC (ϱ = -0.477, P < 0.001).
  • Increasing LAV% correlated with higher visual emphysema grades.
  • Optimal LAV% cutoff values were identified: 6.1 for predicting FEV1/FVC < 70% (AUC=0.764) and 4.7 for moderate-to-severe emphysema (AUC=0.802).
  • AI-based LAV% demonstrated higher sensitivity (77.3%) than visual grading (56.3%) for detecting airflow obstruction.

Conclusions:

  • AI-based LAV% is significantly correlated with FEV1/FVC and visual emphysema grades in routine chest CT.
  • AI-based LAV% measurement can serve as a valuable functional parameter for emphysema evaluation.
  • Integration of AI-based LAV% into routine chest CT analysis is recommended for future clinical application.