ArticleNature communications2025
Predicting orthognathic surgery results as postoperative lateral cephalograms using graph neural networks and diffusion models.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Artificial Intelligence and Digital Technologies in Orthognathic and Reconstructive Maxillofacial Surgery: Data Availability and Evidence Maturity.Dentistry journal · 2026Review
- Predicting 3D Post-Orthodontic Facial Outcomes With a Diffusion Model Trained on Unpaired Datasets.International dental journal · 2026Article
- Evaluation of artificial intelligence-based cephalometric analysis compared to the manual method.Progress in orthodontics · 2026Article
- Evaluation of cephalometric landmarks using artificial intelligence compared to the manual method.Head & face medicine · 2026Article
- Generative Artificial Intelligence: Applications and Future Prospects in Dentistry.International dental journal · 2026Review
- Predictive 3D Modeling of Orthognathic Surgery Outcomes Using Machine Learning Algorithms: A Systematic Review :.Galen medical journal · 2025Review
- Orthognathic Surgery and Artificial Intelligence - Opportunities and Cautions.Annals of maxillofacial surgeryArticle
Corrections and comments
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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