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, w...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-03-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-57669-x |
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| author | 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 Namkug Kim |
| author_facet | 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 Namkug Kim |
| author_sort | In-Hwan Kim |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-787bd071ebc44e73a4a22b3eca2e7488 |
| institution | DOAJ |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-787bd071ebc44e73a4a22b3eca2e74882025-08-20T02:56:11ZengNature PortfolioNature Communications2041-17232025-03-0116111410.1038/s41467-025-57669-xPredicting orthognathic surgery results as postoperative lateral cephalograms using graph neural networks and diffusion modelsIn-Hwan Kim0Jiheon Jeong1Jun-Sik Kim2Jisup Lim3Jin-Hyoung Cho4Mihee Hong5Kyung-Hwa Kang6Minji Kim7Su-Jung Kim8Yoon-Ji Kim9Sang-Jin Sung10Young Ho Kim11Sung-Hoon Lim12Seung-Hak Baek13Jae-Woo Park14Namkug Kim15Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of MedicineDepartment of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of MedicineDepartment of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of MedicineDepartment of Convergence Medicine, University of Ulsan, College of Medicine, Asan Medical CenterDepartment of Orthodontics, Chonnam National University School of DentistryDepartment of Orthodontics, School of Dentistry, Kyungpook National UniversityDepartment of Orthodontics, School of Dentistry, Wonkwang UniversityDepartment of Orthodontics, College of Medicine, Ewha Womans UniversityDepartment of Orthodontics, Kyung Hee University School of DentistryDepartment of Orthodontics, Asan Medical Center, University of Ulsan College of MedicineDepartment of Orthodontics, Asan Medical Center, University of Ulsan College of MedicineDepartment of Orthodontics, Institute of Oral Health Science, Ajou University School of Medicine, Suwon-siDepartment of Orthodontics, College of Dentistry, Chosun UniversityDepartment of Orthodontics, School of Dentistry, Dental Research Institute, Seoul National UniversityDepartment of Convergence Medicine, University of Ulsan, College of Medicine, Asan Medical CenterDepartment of Convergence Medicine, University of Ulsan, College of Medicine, Asan Medical CenterAbstract 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.https://doi.org/10.1038/s41467-025-57669-x |
| spellingShingle | 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 Namkug Kim Predicting orthognathic surgery results as postoperative lateral cephalograms using graph neural networks and diffusion models Nature Communications |
| title | Predicting orthognathic surgery results as postoperative lateral cephalograms using graph neural networks and diffusion models |
| title_full | Predicting orthognathic surgery results as postoperative lateral cephalograms using graph neural networks and diffusion models |
| title_fullStr | Predicting orthognathic surgery results as postoperative lateral cephalograms using graph neural networks and diffusion models |
| title_full_unstemmed | Predicting orthognathic surgery results as postoperative lateral cephalograms using graph neural networks and diffusion models |
| title_short | Predicting orthognathic surgery results as postoperative lateral cephalograms using graph neural networks and diffusion models |
| title_sort | predicting orthognathic surgery results as postoperative lateral cephalograms using graph neural networks and diffusion models |
| url | https://doi.org/10.1038/s41467-025-57669-x |
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