Clinical-oriented 3D visualization and quantitative analysis of gingival thickness using convolutional neural networks and CBCT

Clinical-oriented 3D visualization and quantitative analysis of gingival thickness using convolutional neural networks and CBCT

ObjectiveTraditional gingival thickness (GT) assessment methods provide only point measurements or simple classifications, lacking spatial distribution information. This study aimed to develop a CBCT-based 3D visualization system for gingival thickness using deep learning, providing a novel spatial...

Full description

Saved in:
Bibliographic Details
Main Authors: Lan Yang, ZiCheng Zhu, Yongshan Li, Jieying Huang, Xiaoli Wang, Haoran Zheng, Jiang Chen
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-08-01
Series:Frontiers in Dental Medicine
Subjects:
3D visualization
gingival thickness
deep learning
cone beam computed tomography
implant restoration
Online Access:https://www.frontiersin.org/articles/10.3389/fdmed.2025.1635155/full
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://www.frontiersin.org/articles/10.3389/fdmed.2025.1635155/full

Similar Items