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Related Experiment Video

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Quantitative CT and Artificial Intelligence in Chronic Lung Disease.

Andrea S Oh1, Stephen M Humphries2, Augustine Chung3

  • 1Department of Radiology, UCLA, Los Angeles, CA.

Journal of Thoracic Imaging
|December 19, 2025
PubMed
Summary

Quantitative CT (QCT) and artificial intelligence (AI) offer more objective assessments of chronic lung diseases like COPD and ILD than visual inspection. These advanced techniques improve diagnostic accuracy and patient outcome prediction in thoracic imaging.

Keywords:
artificial intelligencechronic obstructive pulmonary diseaseinterstitial lung diseasequantitative CT

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Area of Science:

  • Radiology
  • Pulmonary Medicine
  • Medical Imaging Analysis

Background:

  • Computed tomography (CT) is vital for diagnosing chronic lung diseases like COPD and ILD.
  • Visual CT assessment is subjective and varies between readers, impacting accuracy.
  • Quantitative CT (QCT) and AI offer objective, reproducible lung disease analysis.

Purpose of the Study:

  • To review QCT and AI applications in diagnosing COPD, ILD, and bronchiolitis obliterans syndrome.
  • To highlight the advantages of QCT and AI over traditional visual CT assessment.
  • To discuss challenges, limitations, and future directions for AI in thoracic imaging.

Main Methods:

  • Review of current literature on QCT and AI in chronic lung disease imaging.
  • Analysis of density and texture-based features for quantitative assessment.
  • Exploration of machine and deep learning algorithms for improved image analysis.

Main Results:

  • QCT features show stronger correlations with lung function and prognosis than visual assessment.
  • AI-based methods provide enhanced robustness and reproducibility in thoracic imaging.
  • These techniques are applicable to COPD, ILD, and post-transplant complications.

Conclusions:

  • QCT and AI represent significant advancements in the objective assessment of chronic lung diseases.
  • Wider adoption of these techniques can improve diagnostic accuracy and patient management.
  • Further research is needed to address current limitations and optimize AI integration in clinical practice.