Global research of artificial intelligence in eyelid diseases: A bibliometric analysis

Xuan Zhang; Ziying Zhou; Yilu Cai; Andrzej Grzybowski; Juan Ye; Lixia Lou

doi:10.1016/j.heliyon.2024.e34979

Global research of artificial intelligence in eyelid diseases: A bibliometric analysis

Heliyon. 2024 Jul 20;10(14):e34979. doi: 10.1016/j.heliyon.2024.e34979. eCollection 2024 Jul 30.

Authors

Xuan Zhang¹, Ziying Zhou¹, Yilu Cai¹, Andrzej Grzybowski², Juan Ye¹, Lixia Lou¹

Affiliations

¹ Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, China.
² Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, 60-836, Poznan, Poland.

Abstract

Purpose: To generate an overview of global research on artificial intelligence (AI) in eyelid diseases using a bibliometric approach.

Methods: All publications related to AI in eyelid diseases from 1900 to 2023 were retrieved from the Web of Science (WoS) Core Collection database. After manual screening, 98 publications published between 2000 and 2023 were finally included. We analyzed the annual trend of publication and citation count, productivity and co-authorship of countries/territories and institutions, research domain, source journal, co-occurrence and evolution of the keywords and co-citation and clustering of the references, using the analytic tool of the WoS, VOSviewer, Wordcloud Python package and CiteSpace.

Results: By analyzing a total of 98 relevant publications, we detected that this field had continuously developed over the past two decades and had entered a phase of rapid development in the last three years. Among these countries/territories and institutions contributing to this field, China was the most productive country and had the most institutions with high productivity, while USA was the most active in collaborating with others. The most popular research domains was Ophthalmology and the most productive journals were Ocular Surface. The co-occurrence network of keywords could be classified into 3 clusters respectively concerned about blepharoptosis, meibomian gland dysfunction and blepharospasm. The evolution of research hotspots is from clinical features to clinical scenarios and from image processing to deep learning. In the clustering analysis of co-cited reference network, cluster "0# deep learning" was the largest and latest, and cluster "#5 meibomian glands visibility assessment" existed for the longest time.

Conclusions: Although the research of AI in eyelid diseases has rapidly developed in the last three years, there are still gaps in this area. Our findings provide researchers with a better understanding of the development of the field and a reference for future research directions.

Keywords: Artificial intelligence; Bibliometric analysis; Eyelid diseases; Global publications.