Global Research Trends in the Detection and Diagnosis of Dental Caries: A Bibliometric Analysis

全球龋病检测与诊断研究趋势:文献计量分析

阅读:4

Abstract

This study aims to provide an overview of the global research trends in the detection and diagnosis of dental caries in the past 20 years. A literature search was conducted in the Scopus Database to retrieve studies on the diagnostic approaches for dental caries published from January 2003 to December 2023. The diagnostic approaches in the retrieved studies were examined and the studies were categorized according to the diagnostic approaches investigated. Bibliometric data including journals, countries, affiliations, authors, and numbers of citations of the publications were summarised. The publications' keyword co-occurrence was analysed using VOSviewer. This bibliometric analysis included 1879 publications investigating seven categories of caries diagnostic approaches, including visual and/or tactile (n = 459; 19%), radiation-based (n = 662; 27%), light-based (n = 771; 32%), ultrasound-based (n = 28; 1%), electric-based (n = 51; 2%), molecular-based (n = 196; 8%) diagnostic approaches, as well as AI-based diagnostic interpretation aids (n = 265; 11%). An increase in the annual number of publications on caries diagnostic approaches was observed in the past 20 years. Caries Research (n = 103) presented the highest number of publications on caries diagnostic approaches. The country with the highest number of publications was the United States (n = 1092). The University of São Paulo was the institution that published the highest number of articles (n = 195). The publication with the highest citation has been cited 932 times. VOS viewer revealed that the most frequently occurring keywords were 'Deep Learning', 'Artificial Intelligence', 'Laser Fluorescence' and 'Radiography'. This bibliometric analysis highlighted an emerging global research trend in the detection and diagnosis approaches for dental caries in the past 20 years. An evident increase in publications on molecular-based caries diagnostic approaches and AI-based diagnostic interpretation aids was perceived over the last 5 years.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。