Application of Artificial Intelligence in Retinopathy of Prematurity From 2010 to 2023: A Bibliometric Analysis
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...
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Wiley
2025-04-01
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| Series: | Health Science Reports |
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| Online Access: | https://doi.org/10.1002/hsr2.70718 |
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| author | Jing Gao Na Fang Yao Xu |
| author_facet | Jing Gao Na Fang Yao Xu |
| author_sort | Jing Gao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2129ab7ce07a4ee79a5b8014df192dbd |
| institution | DOAJ |
| issn | 2398-8835 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Health Science Reports |
| spelling | doaj-art-2129ab7ce07a4ee79a5b8014df192dbd2025-08-20T03:11:58ZengWileyHealth Science Reports2398-88352025-04-0184n/an/a10.1002/hsr2.70718Application of Artificial Intelligence in Retinopathy of Prematurity From 2010 to 2023: A Bibliometric AnalysisJing Gao0Na Fang1Yao Xu2Department of Ophthalmology The First Affiliated Hospital of Soochow University Suzhou ChinaDepartment of Ophthalmology Suzhou TCM Hospital Affiliated to Nanjing University of Chinese Medicine Suzhou ChinaDepartment of Ophthalmology The Fourth Affiliated Hospital of Soochow University Suzhou ChinaABSTRACT 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.https://doi.org/10.1002/hsr2.70718artificial intelligencebibliometric analysisretinopathy of prematurity |
| spellingShingle | Jing Gao Na Fang Yao Xu Application of Artificial Intelligence in Retinopathy of Prematurity From 2010 to 2023: A Bibliometric Analysis Health Science Reports artificial intelligence bibliometric analysis retinopathy of prematurity |
| title | Application of Artificial Intelligence in Retinopathy of Prematurity From 2010 to 2023: A Bibliometric Analysis |
| title_full | Application of Artificial Intelligence in Retinopathy of Prematurity From 2010 to 2023: A Bibliometric Analysis |
| title_fullStr | Application of Artificial Intelligence in Retinopathy of Prematurity From 2010 to 2023: A Bibliometric Analysis |
| title_full_unstemmed | Application of Artificial Intelligence in Retinopathy of Prematurity From 2010 to 2023: A Bibliometric Analysis |
| title_short | Application of Artificial Intelligence in Retinopathy of Prematurity From 2010 to 2023: A Bibliometric Analysis |
| title_sort | application of artificial intelligence in retinopathy of prematurity from 2010 to 2023 a bibliometric analysis |
| topic | artificial intelligence bibliometric analysis retinopathy of prematurity |
| url | https://doi.org/10.1002/hsr2.70718 |
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