ArticleIEEE transactions on medical imaging2023
Simulation of Postoperative Facial Appearances via Geometric Deep Learning for Efficient Orthognathic Surgical Planning.
Article in IEEE transactions on medical imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Artificial Intelligence and Digital Technologies in Orthognathic and Reconstructive Maxillofacial Surgery: Data Availability and Evidence Maturity.Dentistry journal · 2026Review
- A Clinically Compatible Method for Generating Preoperative Finite Element Models to Simulate Facial Appearance and Movements in Orthognathic Surgery.International journal for numerical methods in biomedical engineering · 2026Article
- AI-assisted mesh generation for subject-specific modeling of facial soft tissues.International journal of computer assisted radiology and surgery · 2025Article
- Artificial Intelligence Applications in Pediatric Craniofacial Surgery.Diagnostics (Basel, Switzerland) · 2025Review
- Learning soft tissue deformation from incremental simulations.Medical physics · 2025Article
- Traditional and Artificial Intelligent Methods in Predicting Maxillofacial Soft Tissue Morphology After Orthognathic Surgery: A Narrative Review.International journal of dentistry · 2025Review
- Predictive 3D Modeling of Orthognathic Surgery Outcomes Using Machine Learning Algorithms: A Systematic Review :.Galen medical journal · 2025Review
- Turn Your Vision into Reality-AI-Powered Pre-operative Outcome Simulation in Rhinoplasty Surgery.Aesthetic plastic surgery · 2024Article
- Predicting optimal patient-specific postoperative facial landmarks for patients with craniomaxillofacial deformities.International journal of oral and maxillofacial surgery · 2024Article
- The Accuracy of Three-Dimensional Soft Tissue Simulation in Orthognathic Surgery-A Systematic Review.Journal of imaging · 2024Review
- Accuracy of Artificial Intelligence in Predicting Facial Changes Post-Orthognathic Surgery: A Comprehensive Scoping Review.Journal of clinical and experimental dentistry · 2024Article
- Cataract-1K Dataset for Deep-Learning-Assisted Analysis of Cataract Surgery Videos.Scientific data · 2024Article
- Correspondence attention for facial appearance simulation.Medical image analysis · 2024Article
- 3D printing materials and 3D printed surgical devices in oral and maxillofacial surgery: design, workflow and effectiveness.Regenerative biomaterials · 2024Review
- Predicting Optimal Patient-Specific Postoperative Facial Landmarks for Patients with Craniomaxillofacial Deformities.medRxiv : the preprint server for health sciences · 2023Article
- Prediction of orthognathic surgery plan from 3D cephalometric analysis via deep learning.BMC oral health · 2023Article
- Image generation technology for functional occlusal pits and fissures based on a conditional generative adversarial network.PloS one · 2023Article
- Bidirectional prediction of facial and bony shapes for orthognathic surgical planning.Medical image analysis · 2023Article
- Three-Dimensional Postoperative Results Prediction for Orthognathic Surgery through Deep Learning-Based Alignment Network.Journal of personalized medicine · 2022Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Orthognathic surgery corrects jaw deformities to improve aesthetics and functions. Due to the complexity of the craniomaxillofacial (CMF) anatomy, orthognathic surgery requires precise surgical planning, which involves predicting postoperative changes in facial appearance. To this end, most conventional methods involve simulation with biomechanical modeling methods, which are labor intensive and computationally expensive. Here we introduce a learning-based framework to speed up the simulation of postoperative facial appearances. Specifically, we introduce a facial shape change prediction network (FSC-Net) to learn the nonlinear mapping from bony shape changes to facial shape changes. FSC-Net is a point transform network weakly-supervised by paired preoperative and postoperative data without point-wise correspondence. In FSC-Net, a distance-guided shape loss places more emphasis on the jaw region. A local point constraint loss restricts point displacements to preserve the topology and smoothness of the surface mesh after point transformation. Evaluation results indicate that FSC-Net achieves 15× speedup with accuracy comparable to a state-of-the-art (SOTA) finite-element modeling (FEM) method.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.