Progress on application of deep learning in dental developmental abnormalities and dental development-related diagnosis and treatment
10.12016/j.issn.2096-1456.202440508
- Author:
WANG Siwei
1
;
ZHENG Liwei
1
Author Information
1. State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Pediatric Dentistry, West China Hospital of Stomatology, Sichuan University
- Publication Type:Review
- Keywords:
deep learning;
artificial intelligence;
convolutional neural networks;
dental development;
dental developmental abnormalities;
dental development assessment;
root canal morphology;
tooth eruption
- From:
Journal of Prevention and Treatment for Stomatological Diseases
2025;33(12):1085-1093
- CountryChina
- Language:Chinese
-
Abstract:
Dental developmental abnormalities and dental development-related diagnosis and treatment represents a critical and challenging area of clinical practice. This process spans multiple stages, from diagnosis to the creation of treatment plans, requiring substantial theoretical knowledge and rich clinical experience. In recent years, the development of artificial intelligence (AI), particularly deep learning technologies exemplified by convolutional neural networks, has been facilitated by the abundance of dental clinical image resources. Advancements in AI have provided substantial support for the diagnosis and treatment of oral diseases, significantly enhancing clinical efficiency. Deep learning has numerous applications in developmental abnormalities and dental development-related diagnosis and treatment. First, deep learning can assist in the identification of developmental abnormalities in radiographs and intraoral images, helping dentists make accurate diagnoses. Second, this technology can be used to assess dental development and predict tooth eruption, providing valuable reference for the formulation of personalized treatment plans. Furthermore, deep learning can identify root and root canal morphology, as well as locate challenging root canals, thereby enhancing the dentists' understanding of root canal anatomy and improving the success rate of endodontic treatments. Despite its significant potential in these areas, research in this field remains in the early stage. There are several limitations in the literature, including the inability to implement systematic disease diagnosis and treatment and a lack of multi-center studies. Future research should aim to design multi-center studies and develop deep learning models that integrate disease diagnosis, developmental assessment, and other factors, conducting a comprehensive analysis of multiple variables to further enhance the practical value of these models.
- Full text:2025121815164411496深度学习在牙发育异常及牙发育相关诊疗中的应用研究进展.pdf
