For decades, orthodontic diagnosis and treatment have primarily depended on physical models, imaging techniques, and three-dimensional (3D) imaging technologies. Nevertheless, single-modal data exhibits inherent limitations, including insufficient spatial resolution, lack of tissue specificity, and discontinuous spatiotemporal information. These drawbacks make it challenging to meet the increasingly high requirements for precision and comprehensiveness in modern clinical decision-making. Multimodal data fusion technology, by integrating diverse types of medical data, can fully leverage the unique advantages of each modal data while compensating for their respective deficiencies. This review aims to systematically elaborate on the application value and research progress of multimodal data fusion technology in orthodontics, and to provide insights into its future development directions. A review of the contemporary clinical literature, relating to multimodal data fusion technology, was undertaken using information obtained from the electronic databases PubMed and Web of Science. Eight types of commonly used modal data in orthodontics were identified, each with distinct characteristics. Multimodal data fusion technologies have been widely applied in orthodontic diagnosis and treatment, demonstrating significant application value. Moreover, such technologies have made notable progress in promoting multidisciplinary collaborative treatment involving orthodontics, oral surgery, and prosthodontics. The core advantage of multimodal data fusion technology lies in constructing comprehensive analytical models integrated with multidimensional biomechanical parameters, which can provide quantitative tools for dynamic orthodontic treatment monitoring and outcome evaluation. Multimodal data fusion technology holds remarkable clinical value in the field of orthodontics. It not only supports precise orthodontic treatment but also enhances interdisciplinary collaboration through standardized data platforms. To further advance efficient and precise oral healthcare, future efforts should focus on improving the fusion accuracy of multimodal data and integrating more types of dynamic data. The application of multimodal data fusion technology can effectively address the limitations of single-modal data in orthodontic practice, providing clinicians with more comprehensive and accurate diagnostic information. It facilitates dynamic treatment monitoring and precise outcome evaluation, thereby optimizing clinical decision-making. Additionally, the standardized data platform established by this technology promotes seamless collaboration between orthodontists, oral surgeons, prosthodontists, and other specialists, contributing to the improvement of overall oral healthcare quality.
Feng et al. (Thu,) studied this question.