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

In-Hwan Kim, Jiheon Jeong, Jun-Sik Kim, Jisup Lim, Jin-Hyoung Cho, Mihee Hong, Kyung-Hwa Kang, Minji Kim, Su-Jung Kim, Yoon-Ji Kim, Sang-Jin Sung, Young Ho Kim, Sung-Hoon Lim, Seung-Hak Baek, Jae-Woo Park () and Namkug Kim ()
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In-Hwan Kim: University of Ulsan College of Medicine
Jiheon Jeong: University of Ulsan College of Medicine
Jun-Sik Kim: University of Ulsan College of Medicine
Jisup Lim: Asan Medical Center
Jin-Hyoung Cho: Chonnam National University School of Dentistry
Mihee Hong: Kyungpook National University
Kyung-Hwa Kang: Wonkwang University
Minji Kim: Ewha Womans University
Su-Jung Kim: Kyung Hee University School of Dentistry
Yoon-Ji Kim: University of Ulsan College of Medicine
Sang-Jin Sung: University of Ulsan College of Medicine
Young Ho Kim: Suwon-si
Sung-Hoon Lim: Chosun University
Seung-Hak Baek: Seoul National University
Jae-Woo Park: Asan Medical Center
Namkug Kim: Asan Medical Center

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: 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 Turing test, and a simulation study. Our results demonstrate that GPOSC-Net can accurately predict cephalometric landmark positions and generate high-fidelity synthesized post-operative lateral cephalogram images, providing a valuable tool for surgical planning. By enhancing predictive accuracy and visualization, our model has the potential to improve clinical decision-making and patient communication.

Date: 2025
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DOI: 10.1038/s41467-025-57669-x

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