ArticleMedical physics2017
A clinically validated prediction method for facial soft-tissue changes following double-jaw surgery.
Article in Medical physics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- [Application of sample-efficient neural network in predicting postoperative facial profiles in skeletal class Ⅱ orthognathic patients].Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences · 2026Article
- Artificial Intelligence and Digital Technologies in Orthognathic and Reconstructive Maxillofacial Surgery: Data Availability and Evidence Maturity.Dentistry journal · 2026Review
- Prediction of Three-Dimensional Soft Tissue Changes Following Mandibular PEEK Implantation (Proof of Concept).Journal of personalized medicine · 2026Article
- AI-assisted mesh generation for subject-specific modeling of facial soft tissues.International journal of computer assisted radiology and surgery · 2025Article
- 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
- The Accuracy of Three-Dimensional Soft Tissue Simulation in Orthognathic Surgery-A Systematic Review.Journal of imaging · 2024Review
- Correspondence attention for facial appearance simulation.Medical image analysis · 2024Article
- Simulation of Postoperative Facial Appearances via Geometric Deep Learning for Efficient Orthognathic Surgical Planning.IEEE transactions on medical imaging · 2023Article
- Soft tissue prediction in orthognathic surgery: Improving accuracy by means of anatomical details.PloS one · 2023Article
- A Biomechanical Analysis of the Influence of the Morfology of the Bone Blocks Grafts on the Transfer of Tension or Load to the Soft Tissue by Means of the Finite Elements Method.Materials (Basel, Switzerland) · 2022Article
- A Quantitative and Qualitative Clinical Validation of Soft Tissue Simulation for Orthognathic Surgery Planning.Journal of personalized medicine · 2022Article
- Deep learning for biomechanical modeling of facial tissue deformation in orthognathic surgical planning.International journal of computer assisted radiology and surgery · 2022Article
- Virtual Surgical Planning: Modeling from the Present to the Future.Journal of clinical medicine · 2021Review
- Soft-Tissue Simulation for Computational Planning of Orthognathic Surgery.Journal of personalized medicine · 2021Article
- Accuracy of three-dimensional virtual simulation of the soft tissues of the face in OrtogOnBlender for correction of class II dentofacial deformities: an uncontrolled experimental case-series study.Oral and maxillofacial surgery · 2021Article
- A novel incremental simulation of facial changes following orthognathic surgery using FEM with realistic lip sliding effect.Medical image analysis · 2021Article
- A New Approach of Predicting Facial Changes following Orthognathic Surgery using Realistic Lip Sliding Effect.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2019Article
- Article
- Facial morphology prediction after complete denture restoration based on principal component analysis.Journal of oral biology and craniofacial researchArticle
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Authors and funding
13 authors.
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Abstract
purposeIt is clinically important to accurately predict facial soft-tissue changes prior to orthognathic surgery. However, the current simulation methods are problematic, especially in anatomic regions of clinical significance, e.g., the nose, lips, and chin. We developed a new 3-stage finite element method (FEM) approach that incorporates realistic tissue sliding to improve such prediction.
methodsIn Stage One, soft-tissue change was simulated, using FEM with patient-specific mesh models generated from our previously developed eFace template. Postoperative bone movement was applied on the patient mesh model with standard FEM boundary conditions. In Stage Two, the simulation was improved by implementing sliding effects between gum tissue and teeth using a nodal force constraint scheme. In Stage Three, the result of the tissue sliding effect was further enhanced by reassigning the soft-tissue-bone mapping and boundary conditions using nodal spatial constraint. Finally, our methods have been quantitatively and qualitatively validated using 40 retrospectively evaluated patient cases by comparing it to the traditional FEM method and the FEM with sliding effect, using a nodal force constraint method.
resultsThe results showed that our method was better than the other two methods. Using our method, the quantitative distance errors between predicted and actual patient surfaces for the entire face and any subregions thereof were below 1.5 mm. The overall soft-tissue change prediction was accurate to within 1.1 ± 0.3 mm, with the accuracy around the upper and lower lip regions of 1.2 ± 0.7 mm and 1.5 ± 0.7 mm, respectively. The results of qualitative evaluation completed by clinical experts showed an improvement of 46% in acceptance rate compared to the traditional FEM simulation. More than 80% of the result of our approach was considered acceptable in comparison with 55% and 50% following the other two methods.
conclusionThe FEM simulation method with improved sliding effect showed significant accuracy improvement in the whole face and the clinically significant regions (i.e., nose and lips) in comparison with the other published FEM methods, with or without sliding effect using a nodal force constraint. The qualitative validation also proved the clinical feasibility of the developed approach.
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