Quantitative CT and Artificial Intelligence in Chronic Lung Disease

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.