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

文献计量学 眼睑 数据科学 计算机科学 医学 图书馆学 眼科
作者
X. Zhang,Ziying Zhou,Yilu Cai,Andrzej Grzybowski,Juan Ye,Lixia Lou
出处
期刊:Heliyon [Elsevier BV]
卷期号:10 (14): e34979-e34979
标识
DOI:10.1016/j.heliyon.2024.e34979
摘要

PurposeTo generate an overview of global research on artificial intelligence (AI) in eyelid diseases using a bibliometric approach.MethodsAll 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.ResultsBy 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.ConclusionsAlthough 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.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
上官天宇发布了新的文献求助10
1秒前
hux发布了新的文献求助20
2秒前
水水发布了新的文献求助10
2秒前
leesc94完成签到,获得积分10
2秒前
花火妖妖发布了新的文献求助10
2秒前
初景发布了新的文献求助200
3秒前
3秒前
hahaer完成签到,获得积分10
3秒前
大白完成签到,获得积分20
4秒前
Akim应助Zehn采纳,获得10
4秒前
空空发布了新的文献求助10
4秒前
烟花应助AYUN采纳,获得10
4秒前
xiao发布了新的文献求助10
4秒前
5秒前
赘婿应助科研通管家采纳,获得10
5秒前
zpz发布了新的文献求助10
5秒前
Akim应助科研通管家采纳,获得10
5秒前
5秒前
CodeCraft应助科研通管家采纳,获得10
5秒前
烟花应助科研通管家采纳,获得10
6秒前
6秒前
Hello应助科研通管家采纳,获得10
6秒前
翟文艳发布了新的文献求助10
6秒前
6秒前
搜集达人应助jhj采纳,获得10
6秒前
开朗世立完成签到,获得积分10
7秒前
8秒前
充电宝应助方方采纳,获得10
8秒前
8秒前
9秒前
优雅的水晶男孩完成签到,获得积分10
10秒前
大淘完成签到,获得积分10
12秒前
12秒前
LEONa完成签到,获得积分20
12秒前
贺呵发布了新的文献求助10
13秒前
1319650554发布了新的文献求助10
13秒前
斯文败类应助zkxin采纳,获得10
13秒前
淡定荧应助乖乖猫采纳,获得10
14秒前
15秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 2500
Evidence Summary. Injection (subcutaneous):op- timal administration 1000
悉尼大学博士学位论文,题目:Modelling and testing of one-sided stitched laminated composites. 作者:Kristopher P. Plain 700
Matrix Methods in Data Mining and Pattern Recognition Second Edition 610
Curating Socialism: A Handbook of International Art Exhibitions 1947-1989 530
Soil mites of the family Rhagidiidae (Actinedida: Eupodoidea). Morphology, Systematics, Ecology 520
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7468819
求助须知:如何正确求助?哪些是违规求助? 9063864
关于积分的说明 19323636
捐赠科研通 7089180
什么是DOI,文献DOI怎么找? 3245173
关于科研通互助平台的介绍 2413867
邀请新用户注册赠送积分活动 2230216