Using machine learning for early detection of chronic obstructive pulmonary disease: a narrative review

Area of Science:

• Respiratory Medicine
• Artificial Intelligence
• Medical Informatics

Background:

• Chronic obstructive pulmonary disease (COPD) is a major global health challenge, ranking third in mortality worldwide.
• COPD imposes a substantial burden on individuals and healthcare systems.
• Early detection is crucial for effective management and improved patient outcomes.

Purpose of the Study:

• To review recent advancements in machine learning (ML) for early COPD screening.
• To analyze the practical applications and optimization of ML techniques in COPD detection.
• To provide a framework for future research and development of COPD screening strategies.

Main Methods:

• Comprehensive literature search of domestic and international research on ML for COPD screening.
• Analysis of studies focusing on the application, optimization, and future prospects of ML algorithms.
• Synthesis of findings to identify key trends and challenges.

Main Results:

• Machine learning demonstrates significant potential for early and accurate COPD screening.
• Various ML techniques are being explored, with ongoing efforts to optimize their performance.
• Key areas for optimization include data quality, feature selection, and algorithm generalizability.

Conclusions:

• ML-based approaches are poised to revolutionize early COPD screening.
• Further research is needed to refine ML models and integrate them into clinical practice.
• Establishing a robust scientific foundation will accelerate the development of effective ML-driven COPD screening tools.