Tooth Detection Technology for Oral Disease Diagnosis

DOI

10.2991/978-94-6239-648-7_32How to use a DOI?

Keywords

Dental detection; oral disease diagnosis; image processing; machine learning; deep learning

Abstract

With the constant development of digital medical technology, oral disease is gradually shifting from traditional manual examination to automated detection based on image analysis. Tooth detection technology, as an important part of intelligent dental imaging, plays a crucial role in improving early disease detection rates, reducing subjective diagnostic errors and facilitating personalized treatment plans. This paper reviews tooth detection technology for oral disease diagnosis, categorizing and comparing traditional image processing, machine learning with handcrafted features, and deep learning methods. It highlights research progress, applicable imaging types, and performance metrics in tasks like dental caries detection, periodontal lesion identification, and tooth segmentation. The paper also discusses challenges such as data scarcity, high annotation costs, poor model generalization, and low interpretability. Lastly, it explores future trends, including lightweight models, multimodal fusion, and explainable AI in clinical dentistry, offering insights for future development in intelligent dental diagnostics. This comprehensive analysis provides critical insights for researchers and clinical practitioners, bridging the gap between technological innovation and clinical application.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jie Li
PY  - 2026
DA  - 2026/04/24
TI  - Tooth Detection Technology for Oral Disease Diagnosis
BT  - Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)
PB  - Atlantis Press
SP  - 290
EP  - 300
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-648-7_32
DO  - 10.2991/978-94-6239-648-7_32
ID  - Li2026
ER  -