Norman Noraina Hafizan, Rosli Marshima Mohd, Al-Jaf Nagham Mohammed, Mohammad Norhasmira, Azizan Azliyana, Yusof Mohd Yusmiaidil Putera Mohd
Centre for Paediatric Dentistry and Orthodontic Studies, Faculty of Dentistry, Universiti Teknologi MARA, Sungai Buloh, Malaysia.
Department of Computer Science, Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam, Malaysia.
Imaging Sci Dent. 2025 Jun;55(2):151-164. doi: 10.5624/isd.20240237. Epub 2025 Apr 10.
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.
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.
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.
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.
本研究采用文献计量分析方法，评估人工智能（AI）模型在正畸影像学中的研究趋势、主要贡献者及应用情况。重点阐述1991年至2024年AI在该领域的影响及发展。
从Scopus数据库中提取了130篇文献，涵盖33年的研究。分析内容包括年增长率、引用指标、AI模型应用情况及国际合作。使用VOSviewer进行网络可视化，以绘制研究趋势和共同作者网络。
该研究分析了来自47个来源的96篇出版物，结果显示AI研究呈指数增长，特别是在2010年之后，在2023年达到峰值。研究结果表明年增长率稳定在9.66%，一项基于AI的头影测量分析研究的最高引用次数为138次。卷积神经网络（CNN）和人工神经网络（ANN）在正畸图像分析的AI应用中占主导地位。h指数为23，g指数为38，反映了该领域显著的研究影响力。观察到了强大的国际合作，28.12%的研究涉及跨境研究。
本分析突出了AI在正畸影像学中日益增长的影响力，并强调需要更大的数据集、改进模型的可解释性以及无缝的临床整合。应对这些挑战将进一步加强AI驱动的诊断和治疗计划，为未来的研究和更广泛的临床应用提供指导。
