ArticleInternational journal of computer assisted radiology and surgery2022
Deep learning for biomechanical modeling of facial tissue deformation in orthognathic surgical planning.
Article in International journal of computer assisted radiology and surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 34 citations in OpenAlex.
- Artificial Intelligence and Digital Technologies in Orthognathic and Reconstructive Maxillofacial Surgery: Data Availability and Evidence Maturity.Dentistry journal · 2026Review
- Biomechanical Evaluation of Patient-Specific Titanium Fixation Systems for Segmental Le Fort I Osteotomy: A Finite Element Analysis.Journal of functional biomaterials · 2026Article
- A 15-Year Computational Twin Modeling Study of Botulinum Toxin A Frequency and Age-Related Facial Aging.Aesthetic plastic surgery · 2026Article
- 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
- A Geometric Deep Learning Model for Real-Time Prediction of Knee Joint Biomechanics Under Meniscal Extrusion.Annals of biomedical engineering · 2025Review
- Learning soft tissue deformation from incremental simulations.Medical physics · 2025Article
- Predictive 3D Modeling of Orthognathic Surgery Outcomes Using Machine Learning Algorithms: A Systematic Review :.Galen medical journal · 2025Review
- Application of Artificial Intelligence in Orthognathic Surgery: A Scoping Review.BioMed research international · 2025Article
- Patient-specific reference model estimation for orthognathic surgical planning.International journal of computer assisted radiology and 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
- Correspondence attention for facial appearance simulation.Medical image analysis · 2024Article
- Current Trends in Orthognathic Surgery.Medicina (Kaunas, Lithuania) · 2023Review
- Prediction of orthognathic surgery plan from 3D cephalometric analysis via deep learning.BMC oral health · 2023Article
- Soft tissue prediction in orthognathic surgery: Improving accuracy by means of anatomical details.PloS one · 2023Article
- Discussion on the possibility of multi-layer intelligent technologies to achieve the best recover of musculoskeletal injuries: Smart materials, variable structures, and intelligent therapeutic planning.Frontiers in bioengineering and biotechnology · 2022Review
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
purposeOrthognathic surgery requires an accurate surgical plan of how bony segments are moved and how the face passively responds to the bony movement. Currently, finite element method (FEM) is the standard for predicting facial deformation. Deep learning models have recently been used to approximate FEM because of their faster simulation speed. However, current solutions are not compatible with detailed facial meshes and often do not explicitly provide the network with known boundary type information. Therefore, the purpose of this proof-of-concept study is to develop a biomechanics-informed deep neural network that accepts point cloud data and explicit boundary types as inputs to the network for fast prediction of soft-tissue deformation.
methodsA deep learning network was developed based on the PointNet++ architecture. The network accepts the starting facial mesh, input displacement, and explicit boundary type information and predicts the final facial mesh deformation.
resultsWe trained and tested our deep learning model on datasets created from FEM simulations of facial meshes. Our model achieved a mean error between 0.159 and 0.642 mm on five subjects. Including explicit boundary types had mixed results, improving performance in simulations with large deformations but decreasing performance in simulations with small deformations. These results suggest that including explicit boundary types may not be necessary to improve network performance.
conclusionOur deep learning method can approximate FEM for facial change prediction in orthognathic surgical planning by accepting geometrically detailed meshes and explicit boundary types while significantly reducing simulation time.
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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.