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
研友_LMBPXn发布了新的文献求助10
4秒前
碧海流花完成签到,获得积分10
5秒前
7秒前
润润润完成签到 ,获得积分10
8秒前
米奇完成签到 ,获得积分10
8秒前
大模型应助alpha采纳,获得10
9秒前
华仔应助alpha采纳,获得10
10秒前
搜集达人应助alpha采纳,获得10
10秒前
领导范儿应助alpha采纳,获得10
10秒前
赘婿应助alpha采纳,获得10
10秒前
充电宝应助alpha采纳,获得10
10秒前
顾矜应助alpha采纳,获得10
10秒前
科研通AI6.1应助alpha采纳,获得10
10秒前
Copyright应助alpha采纳,获得10
10秒前
大个应助alpha采纳,获得10
11秒前
dly完成签到 ,获得积分10
16秒前
欢呼半山完成签到 ,获得积分10
26秒前
顺利的源智完成签到,获得积分10
35秒前
fancy发布了新的文献求助10
36秒前
大万发布了新的文献求助10
40秒前
42秒前
何同学应助许某采纳,获得10
47秒前
51秒前
开放黄豆完成签到,获得积分10
1分钟前
在水一方应助科研通管家采纳,获得10
1分钟前
英姑应助科研通管家采纳,获得10
1分钟前
1分钟前
打打应助科研通管家采纳,获得10
1分钟前
尚欣雨完成签到 ,获得积分10
1分钟前
Gu应助明理的鼠标采纳,获得60
1分钟前
芸栖发布了新的文献求助10
1分钟前
fancy完成签到,获得积分10
1分钟前
JImmy完成签到 ,获得积分10
1分钟前
阴暗蘑菇完成签到 ,获得积分10
1分钟前
1分钟前
长岛冰茶完成签到,获得积分10
1分钟前
Martintin发布了新的文献求助10
1分钟前
Lin完成签到 ,获得积分10
1分钟前
芸栖发布了新的文献求助10
1分钟前
打打应助ZHANG采纳,获得10
1分钟前
高分求助中
液晶指向矢仿真分析数据集 8888
Invited Discussant 63O and 64O 1000
Ideology and Meaning-Making under the Putin Regime 750
Petrology and Plate Tectonics 500
Writing Systems 500
A Handbook of User Experience Research & Design in Libraries 400
Understanding Modeling and Simulation of Polymerization Reactions 400
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 计算机科学 化学工程 生物化学 物理 内科学 复合材料 催化作用 光电子学 物理化学 电极 细胞生物学 基因 遗传学
热门帖子
关注 科研通微信公众号,转发送积分 6870326
求助须知:如何正确求助?哪些是违规求助? 8572210
关于积分的说明 18222928
捐赠科研通 6243669
什么是DOI,文献DOI怎么找? 3050999
关于科研通互助平台的介绍 2055433
邀请新用户注册赠送积分活动 2028803