Abstract
BACKGROUND: Optic nerve imaging is crucial for diagnosing and understanding optic neuropathies because it provides detailed visualization of the nerve's structure and pathologies through advanced modalities. This study conducted a bibliometric analysis within the field of optic nerve imaging, aiming to pinpoint the latest research trends and focal points in optic nerve imaging. METHODS: The core literature on optic nerve imaging published between January 1991 and August 2023 was retrieved from the Web of Science Core Collection. The analysis and visualization of scientific productivity and emerging trends were facilitated through the utilization of Bibliometrix software, CiteSpace, Gephi, VOSviewer, R software, and Python. RESULTS: In total, 15,247 publications on optic nerve imaging were included in the analysis. Notably, the top 3 journals contributing to this field were Investigative Ophthalmology & Visual Science, Ophthalmology, and the British Journal of Ophthalmology. This research on optic nerve imaging extended across 97 countries, with the USA leading in research endeavors. Noteworthy burst term analysis revealed that "Segmentation" and "Machine learning" are gaining attention. Additionally, the Latent Dirichlet Allocation model indicated that image processing has been a hotspot in recent years. CONCLUSIONS: This study revealed the research trends, hotspots, and emerging topics in optic nerve imaging through bibliometric analysis and network visualization. At present, the research focus is directed towards employing artificial intelligence for image post-processing. The findings of this study offer valuable insights into future research direction and clinical applications.