Deep learning for 3D cephalometric landmarking with heterogeneous multi-center CBCT dataset.

ABSTRACT: Cephalometric analysis is critically important and common procedure prior to orthodontic treatment and orthognathic surgery. Recently, deep learning approaches have been proposed for automatic 3D cephalometric analysis based on landmarking from CBCT scans. However, these approaches have relied on uniform datasets from a single center or imaging device but without considering patient ethnicity. In addition, previous works have considered a limited number of clinically relevant cephalometric landmarks and the approaches were computationally infeasible, both impairing integration into clinical workflow. Here our aim is to analyze the clinical applicability of a light-weight deep learning neural network for fast localization of 46 clinically significant cephalometric landmarks with multi-cente

SUBMITTER: Sahlsten J 

PROVIDER: S-EPMC11198780 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature