Accuracy and reliability of 3D cephalometric landmark detection with deep learning.

ABSTRACT:

Background

Three-dimensional (3D) landmark detection is essential for assessing craniofacial growth and planning surgeries, such as orthodontic, orthognathic, traumatic, and plastic procedures. This study aimed to develop an automatic 3D landmarking model for oral and maxillofacial regions and to validate its accuracy, robustness and generalizability in both spiral computed tomography (SCT, 41 landmarks) and cone-beam computed tomography (CBCT, 14 landmarks) scans.

Methods

The model was implemented using an optimized lightweight 3D U-Net network architecture. Its accuracy, robustness and generalizability were thoroughly evaluated and validated through a multicenter retrospective diagnostic study. The model was trained and tested on a data set of 480 SCT and 240 CBCT cases. A

SUBMITTER: Liu B 

PROVIDER: S-EPMC12539169 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature