Artificial intelligence in COPD CT images: identification, staging, and quantitation.

Area of Science:

• Pulmonary Medicine
• Medical Imaging
• Artificial Intelligence

Background:

• Chronic obstructive pulmonary disease (COPD) is a major global health concern.
• Current diagnostic strategies for COPD often require advanced methods.
• Medical imaging plays a crucial role in understanding COPD's complex nature.

Purpose of the Study:

• To review the advancements and applications of artificial intelligence (AI) in COPD diagnosis and management.
• To explore AI's role in analyzing medical imaging for COPD identification, staging, and phenotyping.
• To discuss the potential and challenges of integrating AI into clinical COPD care.

Main Methods:

• Review of current literature on AI applications in COPD medical imaging.
• Focus on machine learning and deep learning techniques.
• Analysis of AI's utility in assessing emphysema, airway dynamics, and vascular structures in COPD patients.

Main Results:

• AI demonstrates significant potential in identifying and staging COPD through medical imaging analysis.
• Machine learning and deep learning models offer insights into COPD-related imaging phenotypes like emphysema and vascular changes.
• AI integration faces challenges related to data complexity and clinical workflow adaptation.

Conclusions:

• AI, particularly deep learning, is poised to transform COPD diagnosis and management via advanced medical imaging analysis.
• Future innovations in AI for COPD imaging, coupled with interdisciplinary collaboration, promise significant breakthroughs in patient care.
• AI offers a powerful tool for a more precise and personalized approach to COPD.