ArticlePloS one2018
A novel soft tissue prediction methodology for orthognathic surgery based on probabilistic finite element modelling.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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25 citing papers in PubMed, 60 citations in OpenAlex.
- [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
- Finite element analysis of the nonlinear response of oral mucosa in labial tissue induced by spherical versus square self-ligating brackets.BMC oral health · 2026Article
- Comparison of nasolabial changes following Le Fort Ⅰ maxillary advancement versus Medpor implantation in skeletal class Ⅲ patients.Head & face medicine · 2026Article
- An adaptive extended radial basis function based interval analysis method for structural engineering solutions.PloS one · 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
- Does artificial intelligence predict orthognathic surgical outcomes better than conventional linear regression methods?The Angle orthodontist · 2024Article
- Correspondence attention for facial appearance simulation.Medical image analysis · 2024Article
- Spring-assisted posterior vault expansion: a parametric study to improve the intracranial volume increase prediction.Scientific reports · 2023Article
- 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
- Bidirectional prediction of facial and bony shapes for orthognathic surgical planning.Medical image analysis · 2023Article
- A Quantitative and Qualitative Clinical Validation of Soft Tissue Simulation for Orthognathic Surgery Planning.Journal of personalized medicine · 2022Article
- A Fully Automatic Postoperative Appearance Prediction System for Blepharoptosis Surgery with Image-based Deep Learning.Ophthalmology science · 2022Article
- Article
- Deep learning for biomechanical modeling of facial tissue deformation in orthognathic surgical planning.International journal of computer assisted radiology and surgery · 2022Article
- Parametrizing the genioplasty: a biomechanical virtual study on soft tissue behavior.International journal of computer assisted radiology and surgery · 2022Article
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Repositioning of the maxilla in orthognathic surgery is carried out for functional and aesthetic purposes. Pre-surgical planning tools can predict 3D facial appearance by computing the response of the soft tissue to the changes to the underlying skeleton. The clinical use of commercial prediction software remains controversial, likely due to the deterministic nature of these computational predictions. A novel probabilistic finite element model (FEM) for the prediction of postoperative facial soft tissues is proposed in this paper. A probabilistic FEM was developed and validated on a cohort of eight patients who underwent maxillary repositioning and had pre- and postoperative cone beam computed tomography (CBCT) scans taken. Firstly, a variables correlation assessed various modelling parameters. Secondly, a design of experiments (DOE) provided a range of potential outcomes based on uniformly distributed input parameters, followed by an optimisation. Lastly, the second DOE iteration provided optimised predictions with a probability range. A range of 3D predictions was obtained using the probabilistic FEM and validated using reconstructed soft tissue surfaces from the postoperative CBCT data. The predictions in the nose and upper lip areas accurately include the true postoperative position, whereas the prediction under-estimates the position of the cheeks and lower lip. A probabilistic FEM has been developed and validated for the prediction of the facial appearance following orthognathic surgery. This method shows how inaccuracies in the modelling and uncertainties in executing surgical planning influence the soft tissue prediction and it provides a range of predictions including a minimum and maximum, which may be helpful for patients in understanding the impact of surgery on the face.
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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.