Application of Artificial Intelligence in Retinopathy of Prematurity From 2010 to 2023: A Bibliometric Analysis - Jing Gao; Na Fang; Yao Xu (Health Science Reports)
ABSTRACT Background and Aims Retinopathy of prematurity (ROP) remains a leading cause of childhood blindness worldwide. In recent years, artificial intelligence (AI) has emerged as a powerful tool for the screening and management of ROP. This study aimed to investigate the evolving and longitudinal publication patterns related to AI in ROP using bibliometric methodologies. Methods We conducted a descriptive analysis of AI in ROP documents retrieved from the Web of Science database up to September 10, 2023. Data analysis and visualization were performed using Bibliometrix and VOSviewer, covering publications, journals, authors, institutions, countries, collaboration networks, keywords, and trending topics. Results Our analysis of 188 publications on AI in ROP revealed an average of 7.62 authors per document and a notable increase in annual publications since 2017. The United States (98/188), Oregon Health & Science University (66/188), Investigative Ophthalmology & Visual Science (29/188) and author Michael F. Chiang (60/188) led contributions. A prominent 21‐country network emerged as the largest in country‐level coauthorship. Key technical terms included “artificial intelligence,” “deep learning,” “machine learning,“ and “telemedicine,” with a recent shift from “feature selection” to “deep learning,” “machine learning” and “fundus images“ in trending topics. Conclusion Our bibliometric analysis highlights advancements in AI research on ROP, focusing on key publication characteristics, major contributors, and emerging trends. The findings indicate that AI in ROP is a rapidly growing field. Future studies should focus on addressing the clinical implementation and ethical concerns of AI in ROP.