亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

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
1秒前
娃哈哈读研版完成签到,获得积分10
2秒前
3秒前
李学东完成签到,获得积分10
6秒前
8秒前
浮生发布了新的文献求助10
9秒前
裴瑞志完成签到,获得积分10
12秒前
小马甲应助精明金毛采纳,获得10
17秒前
路漫漫其修远兮完成签到 ,获得积分10
17秒前
18秒前
小明无敌完成签到,获得积分20
24秒前
田様应助小明无敌采纳,获得10
28秒前
kkk完成签到 ,获得积分10
38秒前
39秒前
39秒前
Beto发布了新的文献求助10
42秒前
忧伤的摩托完成签到,获得积分20
44秒前
NexusExplorer应助Beto采纳,获得30
50秒前
54秒前
Xiaoxiao完成签到,获得积分10
55秒前
YBR完成签到 ,获得积分10
59秒前
xxx完成签到,获得积分10
59秒前
1分钟前
高大醉易发布了新的文献求助10
1分钟前
善良太阳完成签到,获得积分10
1分钟前
1分钟前
羲成完成签到,获得积分10
1分钟前
初景应助忐忑的夏蓉采纳,获得20
1分钟前
阿花阿花发布了新的文献求助10
1分钟前
踏实夜安应助雾陆炜采纳,获得10
1分钟前
小神仙完成签到 ,获得积分10
1分钟前
yq完成签到,获得积分10
1分钟前
英俊的未来完成签到 ,获得积分10
1分钟前
所所应助科研通管家采纳,获得10
1分钟前
嘻嘻哈哈应助科研通管家采纳,获得10
1分钟前
斯文败类应助wgm采纳,获得10
1分钟前
Hello应助雾陆炜采纳,获得10
1分钟前
初景应助忐忑的夏蓉采纳,获得20
1分钟前
1分钟前
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
Cronologia da história de Macau 5000
Petrology and Plate Tectonics 800
Electrode Potentials 550
Matrix Methods in Data Mining and Pattern Recognition 510
Association of Reentry Well-Being with Psychological Distress, Employment, and Housing Instability 15-Months After Incarceration 500
Trees of tropical Asia : an illustrated guide to diversity 500
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 7038343
求助须知:如何正确求助?哪些是违规求助? 8705957
关于积分的说明 18442141
捐赠科研通 6545912
什么是DOI,文献DOI怎么找? 3115585
关于科研通互助平台的介绍 2197657
邀请新用户注册赠送积分活动 2090962