Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Feb 24, 2022
Date Accepted: Apr 4, 2022
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Emerging Trends and Research Foci in Artificial Intelligence for Retinal Diseases: A Bibliometric and Visualized Study
ABSTRACT
Background:
Patients with retinal diseases may exhibit serious complications that cause severe visual impairment owing to a lack of awareness of retinal diseases and limited medical resources. Understanding how artificial intelligence (AI) is used to make predictions and analysis is a very active area of research within the retinal disease learning community. In this study, the relevant Science Citation Index (SCI) literature on the AI of retinal diseases from 2012 to 2021 was integrated and analyzed.
Objective:
This study aimed to gain insights into the overall application of AI technology to the research of retinal diseases from the set time and space dimensions.
Methods:
Citation data downloaded from the Web of Science Core Collection database for use in AI in retinal disease publications published from January 1, 2012, to December 31, 2021, were considered for research. Information retrieval was analyzed using the online analysis platforms of literature metrology: CiteSpace V, and VOS viewer.
Results:
A total of 197 institutions from 86 countries contributed to relevant publications, of which China had the largest number and the University of London journal had the highest h-index. The reference clusters of SCI papers were clustered into 12 categories. "Deep learning" was the cluster with the widest range of co-cited references. The burst keywords represented the research frontiers in 2018-2021, which were eye disease and enhancement.
Conclusions:
This study provides a systematic analysis method in the literature regarding AI in retinal diseases. Bibliometric analysis enabled the results to be objective and comprehensive. In future, high-quality retinal image-forming technology with strong stability and clinical applicability will continue to be encouraged.
Citation
