Int J Stomatol

Int J Stomatol ›› 2025, Vol. 52 ›› Issue (6): 764-770.doi: 10.7518/gjkq.2025103

• Orthodontics • Previous Articles     Next Articles

Application and research progress of multimodal data fusion in orthodontics

Jian Zhang(),Xue Bai,Xiaoqian He,Zhenlin Ge()   

  1. Dept. of Orthodontics, Hospital of Stomatology, Lanzhou University, Lanzhou 730000, China
  • Received:2024-09-29 Revised:2025-02-19 Online:2025-11-01 Published:2025-10-23
  • Contact: Zhenlin Ge E-mail:220220929731@lzu.edu.cn;gezhl@lzu.edu.cn

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Abstract:

With the advancement of digital technology, multimodal data fusion has been widely applied in orthodontics. This technology integrates patient clinical data, imaging data, oral scanning data, 3D facial scan data, biomechanical data, and electronic facial arch data to improve the accuracy of orthodontic diagnosis and the effectiveness of clinical treatment. This study aims to explore the current application status, potential value, challenges, and future development directions of multimodal data fusion in orthodontic research and clinical practice.

Key words: multimodal data fusion, orthodontics, accurate diagnosis, personalized treatment

CLC Number: 

  • R783.5

TrendMD: 

Tab 1

Comparison of the 3 fusion methods"

方法名称适用范围优点缺点实现难度
特征级融合（早期融合）图像、文本、音频等多种模态数据的融合可以捕捉不同模态间的低级关联信息可能面临过拟合风险，在跨视图动态建模方面可能表现不佳中等
决策级融合（后期融合）多用于分类、决策任务，如医疗诊断、安防系统能更好地对特定视图的动态进行建模在探索动态交互和低级模态互动方面存在不足较低
混合级融合根据不同的应用场景和数据特点灵活应用适应不同模态的数据，提升结果的质量、准确性和鲁棒性，灵活性高实现较为复杂，需要设计合理的融合机制较高

Tab 2

Key technical challenges and countermeasures"

主要技术挑战应对策略
数据获取与处理的复杂性数据标准化、数据预处理、专业设备和技术
数据融合算法复杂程度高算法优化、计算资源增强、可解释性提升
数据安全与隐私保护问题数据加密、隐私保护技术、法规和政策
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