Predicting orthognathic surgery results as postoperative lateral cephalograms using graph neural networks and diffusion models.

ABSTRACT: Orthognathic surgery, or corrective jaw surgery, is performed to correct severe dentofacial deformities and is increasingly sought for cosmetic purposes. Accurate prediction of surgical outcomes is essential for selecting the optimal treatment plan and ensuring patient satisfaction. Here, we present GPOSC-Net, a generative prediction model for orthognathic surgery that synthesizes post-operative lateral cephalograms from pre-operative data. GPOSC-Net consists of two key components: a landmark prediction model that estimates post-surgical cephalometric changes and a latent diffusion model that generates realistic synthesizes post-operative lateral cephalograms images based on predicted landmarks and segmented profile lines. We validated our model using diverse patient datasets, a visual Tur

SUBMITTER: Kim IH 

PROVIDER: S-EPMC11911408 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature