近十五年圆锥角膜生物力学研究领域的文献计量分析

The Bibliometric Analysis of the Research Trends of Biomechanical Studies on Keratoconus in the Past Fifteen Years

目的:基于Web of Science(WOS)数据库的收录情况,分析圆锥角膜生物力学研究领域的现状和热点。方法:以近十五年(2009—2023年)WOS核心数据库收录的关于圆锥角膜生物力学研究领域的文献为分析对象,利用CiteSpace软件,运用文献计量学方法,从文献的年发文量、国家及机构来源、刊文期刊分布及关键词突现、共被引文献分析等角度对其进行可视化分析。结果:共检索到557篇文献,发文量最多的国家是美国及中国;发文量最多的研究机构是圣保罗大学、利物浦大学、北京航空航天大学;发文最多的期刊为《Cornea》以及《Investigative Ophthalmology Visual Science》。研究的历程分为2个阶段:第一阶段的研究主要基于眼反应分析仪(ORA)进行,围绕角膜滞后(CH)和角膜阻力因子(CRF),研究主要使用受试者工作特征曲线这一统计工具探索CH和CRF对圆锥角膜的早期诊断效能,以及研究角膜交联、激光手术前后CH和CRF的变化。第二阶段的研究将机器学习与可视化角膜生物力学分析仪(Corvis ST)相结合,在Corvis ST测量的多种角膜生物力学参数基础上进行回归分析、参数调优,或通过有限元模型研究后引入新参数,并尝试通过这些参数实现对圆锥角膜的早期诊断。目前的研究热点在于利用人工智能结合多模态诊断数据对圆锥角膜进行分层诊断。结论:圆锥角膜生物力学研究领域在过去15年间取得了显著进展,从依赖ORA评估CH和CRF的初期,逐步发展到结合机器学习与Corvis ST进行参数调优,未来人工智能整合圆锥角膜多模态数据具有较大的发展空间。

Objective:To analyze the current state and hotspots in the field of keratoconus biomechanics research,based on the data from Web of Science(WOS)database.Methods:A literature study was conducted on publications during the past fifteen years(2009—2023)in the WOS core database related to keratoconus biomechanics.Using CiteSpace software and bibliometric methods,the visual analysis was performed based on annual publication volume,country and institutional origins,journal distribution,key emergent terms,and co-citation analysis.Results:A total of 557 publications were retrieved.The United States and China had the highest publication volumes.The leading institutions were the University of São Paulo,the University of Liverpool,and Beihang University.The most prolific journals were Cornea and Investigative Ophthalmology Visual Science.Studies can be divided into two phases.The first phase focused on the ocular response analyzer(ORA),revolving around corneal hysteresis(CH),and corneal resistance factor(CRF).Studies utilized the receiver operating characteristic(ROC)curve as a statistical method,to investigate the diagnostic efficacy of CH and CRF in the early detection of keratoconus.Additionally,it also examined the alterations in CH and CRF before and after corneal cross-linking and laser surgery.The second phase combined machine learning with corneal visualization Scheimpflug technology(Corvis ST)for regression analysis,parameter tuning,and finite element model studies.Current research hotspots involve the use of artificial intelligence to integrate multimodal diagnostic data for stratified diagnosis of keratoconus.Conclusions:In the last fifteen years,remarkable strides have been achieved in the domain of keratoconus biomechanics,transitioning from an early dependence on ORA for evaluating CH and CRF,to an advanced stage that merges machine learning algorithms with the high-resolution visualization of the Corvis ST device for fine-tuning parameters.The prospective integration of artificial intelligence with a multitude of data modes in keratoconus investigations portends immense potential for groundbreaking advancements.