Integration of artificial intelligence in orthodontic imaging: A bibliometric analysis of research trends and applications.

IF 2.1 Q3 DENTISTRY, ORAL SURGERY & MEDICINE
Imaging Science in Dentistry Pub Date : 2025-06-01 Epub Date: 2025-04-10 DOI:10.5624/isd.20240237
Noraina Hafizan Norman, Marshima Mohd Rosli, Nagham Mohammed Al-Jaf, Norhasmira Mohammad, Azliyana Azizan, Mohd Yusmiaidil Putera Mohd Yusof
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引用次数: 0

Abstract

Purpose: This study employs bibliometric analysis to evaluate research trends, key contributors, and applications of artificial intelligence (AI) models in orthodontic imaging. It highlights the impact and evolution of AI in this field from 1991 to 2024.

Material and methods: A total of 130 documents were extracted from the Scopus database, spanning 33 years of research. The analysis examined annual growth rates, citation metrics, AI model adoption, and international collaborations. Network visualization was performed using VOSviewer to map research trends and co-authorship networks.

Results: The study analyzed 96 publications from 47 sources, revealing exponential growth in AI research-particularly after 2010, with a peak in 2023. The findings show a steady annual growth rate of 9.66% and a maximum citation count of 138 for an AI-based cephalometric analysis study. Convolutional neural networks (CNNs) and artificial neural networks (ANNs) dominate AI applications in orthodontic image analysis. An h-index of 23 and a g-index of 38 reflect the field's significant research impact. Strong international collaborations were observed, with 28.12% of studies involving cross-border research.

Conclusion: This analysis highlights the growing influence of AI in orthodontic imaging and emphasizes the need for larger datasets, improved model interpretability, and seamless clinical integration. Addressing these challenges will further enhance AI-driven diagnostics and treatment planning, guiding future research and broader clinical applications.

人工智能在正畸成像中的集成:研究趋势和应用的文献计量学分析。
目的:本研究采用文献计量分析的方法评估人工智能(AI)模型在正畸成像中的研究趋势、主要贡献者和应用。它突出了1991年至2024年人工智能在该领域的影响和演变。材料和方法:从Scopus数据库中提取130篇文献,历时33年。该分析考察了年增长率、引用指标、人工智能模型采用和国际合作。使用VOSviewer进行网络可视化,绘制研究趋势和合著者网络。结果:该研究分析了来自47个来源的96份出版物,揭示了人工智能研究的指数增长——特别是在2010年之后,2023年达到顶峰。研究结果显示,基于人工智能的头颅测量分析研究的年增长率为9.66%,最大引用数为138。卷积神经网络(cnn)和人工神经网络(ann)是人工智能在正畸图像分析中的主要应用。h指数为23,g指数为38,反映出该领域具有显著的研究影响力。观察到强有力的国际合作,28.12%的研究涉及跨境研究。结论:该分析强调了人工智能在正畸成像中的影响力越来越大,并强调需要更大的数据集、改进的模型可解释性和无缝的临床整合。应对这些挑战将进一步加强人工智能驱动的诊断和治疗计划,指导未来的研究和更广泛的临床应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Imaging Science in Dentistry
Imaging Science in Dentistry DENTISTRY, ORAL SURGERY & MEDICINE-
CiteScore
2.90
自引率
11.10%
发文量
42
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