Artificial intelligence applications in ophthalmic optical coherence tomography: a 12-year bibliometric analysis

Area of Science:

• Ophthalmology
• Computer Science
• Artificial Intelligence

Background:

• Ophthalmic imaging relies heavily on Optical Coherence Tomography (OCT).
• Artificial Intelligence (AI) is increasingly integrated into medical imaging analysis.
• The application of AI in ophthalmic OCT is a rapidly evolving research area.

Purpose of the Study:

• To explore the current applications of AI in ophthalmic OCT imaging.
• To identify research frontiers in AI-driven ophthalmic OCT.
• To analyze global research trends and key contributors.

Main Methods:

• Bibliometric analysis of 877 articles from 65 countries (2012-2023) using Web of Science Core Collection.
• Analysis conducted with CiteSpace 6.2.R2 Advanced software.
• Focus on high-impact articles and keyword trends.

Main Results:

• Significant growth in publications on AI in ophthalmic OCT, with the United States and China as leading contributors.
• Key disciplines include Ophthalmology, Computer Science, and AI.
• Emerging hot keywords post-2018 include deep learning (DL), AI, macular degeneration, and automatic segmentation.

Conclusions:

• The annual output of research on AI in ophthalmic OCT is increasing exponentially.
• The United States leads in research output and impact.
• Research focus has shifted from traditional machine learning (ML) to deep learning (DL) for tasks like automatic segmentation and diagnosis, with OCT angiography (OCTA) and computer-aided diagnosis (CAD) gaining prominence.