⚡ Quick Summary

This bibliometric analysis explores the global trends and research hotspots in the application of artificial intelligence (AI) for computed tomography (CT) detection of chronic obstructive pulmonary disease (COPD) from 2012 to 2024. The study reveals a significant growth in publications, with an annual growth rate of 37.83%.

🔍 Key Details

• 📊 Publications analyzed: 189 publications
• 🌍 Leading countries: United States (53), China (51), Germany (13)
• 🏫 Most prolific institution: University of Iowa (69 publications)
• 📝 Key journals: Scientific Reports, Radiology
• 👨‍🔬 Most prolific author: Hoffman Eric A. (16 publications)

🔑 Key Takeaways

• 📈 Growth trend: Annual growth rate of 37.83% in AI applications for COPD detection.
• 🌐 Research hotspots: Emerging topics include “quantitative imaging,” “low dose CT,” and “body mass index.”
• 🏆 Leading contributors: University of Iowa and Harvard University are at the forefront of research.
• 💡 Focus on patient assessment: Growing interest in comprehensive assessments and population studies.
• 🔍 Future research directions: Differentiating COPD from other lung diseases for personalized treatment.

📚 Background

Chronic obstructive pulmonary disease (COPD) is a major global health concern, characterized by progressive inflammation of the lungs. The integration of artificial intelligence in medical imaging, particularly computed tomography, offers promising avenues for enhancing the detection and management of COPD. This study aims to provide a comprehensive overview of the current landscape of research in this field.

🗒️ Study

The analysis involved a systematic review of publications related to AI applications for CT detection of COPD from 2012 to 2024, sourced from the Web of Science Core Collection (WoSCC). Utilizing bibliometric tools such as VOSviewer, CiteSpace, and the R package “bibliometrix,” the researchers identified key contributors and emerging trends in the field.

📈 Results

The findings indicate a robust increase in research output, with a total of 189 publications and an impressive annual growth rate of 37.83%. The United States emerged as the leading contributor, followed closely by China. Notably, the University of Iowa was identified as the most prolific institution, highlighting its significant role in advancing research in AI applications for COPD detection.

🌍 Impact and Implications

This bibliometric analysis sheds light on the evolving landscape of AI in the detection of COPD, emphasizing the importance of quantitative imaging and comprehensive patient assessments. The insights gained from this study could guide future research efforts, particularly in differentiating COPD from other lung diseases, ultimately leading to more personalized treatment approaches and improved patient outcomes.

🔮 Conclusion

The study underscores the growing significance of AI in the realm of medical imaging for COPD detection. As research continues to expand, there is a clear opportunity for innovation in patient assessment and treatment strategies. We encourage ongoing exploration in this promising field to enhance the quality of care for individuals affected by COPD.

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