Deep learning-based multimodal data fusion: applications and research progress in oral diseases
10.12016/j.issn.2096-1456.20260041
- Author:
HE Qinxuan
1
,
1
;
CAO Zhicheng
2
;
WANG Wei
3
Author Information
1. School of Basic Medicine, Air Force Medical University
2. Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology
3. National Clinical Research Center for Oral Diseases, State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Shaanxi Clinical Research Center for Oral Diseases, Department of Operative Dentistry and Endodontics, School of Stomatology, the Forth Military Medical University
- Publication Type:Review
- Keywords:
artificial intelligence;
deep learning;
multimodal data fusion technology;
fusion strategies;
oral cancer;
periodontitis;
dental caries;
temporomandibular disorders
- From:
Journal of Prevention and Treatment for Stomatological Diseases
2026;34(10):1009-1020
- CountryChina
- Language:Chinese
-
Abstract:
With the rapid advancement of artificial intelligence, deep learning-based multimodal data fusion technology has found increasing applications in the field of dental and stomatological diseases, and it has demonstrated great potential. This technology constructs deep neural networks to automatically extract and fuse features from multimodal data, such as clinical text information, medical imaging, intraoral and extraoral digital scanning records, and histopathological data. By fully leveraging the complementary information among these diverse data sources, it substantially enhances the accuracy of oral disease diagnosis, the rationality of treatment planning, and the reliability of prognosis prediction. This survey systematically explores the core advantages of deep learning in multimodal data fusion; outlines key main strategies, such as early fusion, intermediate fusion, and late fusion along with their applicable scenarios; and summarizes the specific applications and latest research progress of this technology in oral diseases, including oral cancer, periodontitis, dental caries, and temporomandibular disorders. We also highlight critical challenges in its clinical translation, such as complexity in multimodal data alignment, scarcity of high-quality annotated data, insufficient model interpretability, and privacy and security issues. In the future, this technology is expected to develop toward new learning paradigms, intelligentization, and adaptive fusion while integrating explainable artificial intelligence to enhance clinical credibility. Ultimately, large-scale, multicenter clinical validation systems will be required to ensure its safe and effective deployment in clinical practice.
- Full text:2026093010584971971基于深度学习的多模态数据融合技术在口腔疾病中的应用及研究进展.pdf
