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Du W, Wang H, Zhao C, Cui Z, Li J, Zhang W, Yu Y, Peng X. Postoperative facial prediction for mandibular defect based on surface mesh deformation. JOURNAL OF STOMATOLOGY, ORAL AND MAXILLOFACIAL SURGERY 2024:101973. [PMID: 39089509 DOI: 10.1016/j.jormas.2024.101973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Revised: 07/12/2024] [Accepted: 07/17/2024] [Indexed: 08/04/2024]
Abstract
OBJECTIVES This study aims to introduce a novel predictive model for the post-operative facial contours of patients with mandibular defect, addressing limitations in current methodologies that fail to preserve geometric features and lack interpretability. METHODS Utilizing surface mesh theory and deep learning, our model diverges from traditional point cloud approaches by employing surface triangular mesh grids. We extract latent variables using a Mesh Convolutional Restricted Boltzmann Machines (MCRBM) model to generate a three-dimensional deformation field, aiming to enhance geometric information preservation and interpretability. RESULTS Experimental evaluations of our model demonstrate a prediction accuracy of 91.2 %, which represents a significant improvement over traditional machine learning-based methods. CONCLUSIONS The proposed model offers a promising new tool for pre-operative planning in oral and maxillofacial surgery. It significantly enhances the accuracy of post-operative facial contour predictions for mandibular defect reconstructions, providing substantial advancements over previous approaches.
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Affiliation(s)
- Wen Du
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Beijing Key Laboratory of Digital Stomatology, NHC Key Laboratory of Digital Stomatology, China
| | - Hao Wang
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Beijing Key Laboratory of Digital Stomatology, NHC Key Laboratory of Digital Stomatology, China
| | - Chenche Zhao
- College of Engineering, Peking University, China
| | - Zhiming Cui
- School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China
| | - Jiaqi Li
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Beijing Key Laboratory of Digital Stomatology, NHC Key Laboratory of Digital Stomatology, China
| | - Wenbo Zhang
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Beijing Key Laboratory of Digital Stomatology, NHC Key Laboratory of Digital Stomatology, China
| | - Yao Yu
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Beijing Key Laboratory of Digital Stomatology, NHC Key Laboratory of Digital Stomatology, China
| | - Xin Peng
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Beijing Key Laboratory of Digital Stomatology, NHC Key Laboratory of Digital Stomatology, China.
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Olejnik A, Verstraete L, Croonenborghs TM, Politis C, Swennen GRJ. The Accuracy of Three-Dimensional Soft Tissue Simulation in Orthognathic Surgery-A Systematic Review. J Imaging 2024; 10:119. [PMID: 38786573 PMCID: PMC11122049 DOI: 10.3390/jimaging10050119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Revised: 04/26/2024] [Accepted: 05/07/2024] [Indexed: 05/25/2024] Open
Abstract
Three-dimensional soft tissue simulation has become a popular tool in the process of virtual orthognathic surgery planning and patient-surgeon communication. To apply 3D soft tissue simulation software in routine clinical practice, both qualitative and quantitative validation of its accuracy are required. The objective of this study was to systematically review the literature on the accuracy of 3D soft tissue simulation in orthognathic surgery. The Web of Science, PubMed, Cochrane, and Embase databases were consulted for the literature search. The systematic review (SR) was conducted according to the PRISMA statement, and 40 articles fulfilled the inclusion and exclusion criteria. The Quadas-2 tool was used for the risk of bias assessment for selected studies. A mean error varying from 0.27 mm to 2.9 mm for 3D soft tissue simulations for the whole face was reported. In the studies evaluating 3D soft tissue simulation accuracy after a Le Fort I osteotomy only, the upper lip and paranasal regions were reported to have the largest error, while after an isolated bilateral sagittal split osteotomy, the largest error was reported for the lower lip and chin regions. In the studies evaluating simulation after bimaxillary osteotomy with or without genioplasty, the highest inaccuracy was reported at the level of the lips, predominantly the lower lip, chin, and, sometimes, the paranasal regions. Due to the variability in the study designs and analysis methods, a direct comparison was not possible. Therefore, based on the results of this SR, guidelines to systematize the workflow for evaluating the accuracy of 3D soft tissue simulations in orthognathic surgery in future studies are proposed.
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Affiliation(s)
- Anna Olejnik
- Division of Maxillofacial Surgery, Department of Surgery, AZ Sint-Jan, Ruddershove 10, 8000 Bruges, Belgium
- Maxillofacial Surgery Unit, Department of Head and Neck Surgery, Craniomaxillofacial Center for Children and Young Adults, Regional Specialized Children’s Hospital, ul. Zolnierska 18A, 10-561 Olsztyn, Poland
| | - Laurence Verstraete
- Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, 3000 Leuven, Belgium
| | - Tomas-Marijn Croonenborghs
- Division of Maxillofacial Surgery, Department of Surgery, AZ Sint-Jan, Ruddershove 10, 8000 Bruges, Belgium
| | - Constantinus Politis
- Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, 3000 Leuven, Belgium
| | - Gwen R. J. Swennen
- Division of Maxillofacial Surgery, Department of Surgery, AZ Sint-Jan, Ruddershove 10, 8000 Bruges, Belgium
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Almarhoumi AA. Accuracy of Artificial Intelligence in Predicting Facial Changes Post-Orthognathic Surgery: A Comprehensive Scoping Review. J Clin Exp Dent 2024; 16:e624-e633. [PMID: 38988747 PMCID: PMC11231886 DOI: 10.4317/jced.61500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Accepted: 04/10/2024] [Indexed: 07/12/2024] Open
Abstract
Background Accurate prediction of facial soft tissue changes post-orthognathic surgery is crucial for treatment planning and patient communication. Current models pose limitations due to the complexity of facial biomechanics and individual variances. Artificial intelligence (AI) has emerged as an important tool in many disciplines, including the dental field. Objectives The aim of this scoping review is to assess the accuracy of AI in predicting facial changes post-orthognathic surgery in comparison to traditional models. Explore the strengths and limitations of the current AI models. Material and Methods Following PRISMA-DTA guidelines, a comprehensive search was conducted manually and through Medline, Embase, Web of Science, Scopus, and Google Scholar databases was conducted, focusing on studies that applied AI models with various machine learning and deep learning algorithms for post-surgical outcome prediction. Selection criteria were based on the PICO format, emphasizing studies that compared AI-predicted outcomes with actual post-surgical results. Literature was searched until January 31, 2024. Results The initial search result yielded 1579 records. After screening and assessment for eligibility, seven studies met the inclusion criteria, with publication dates ranging from 2009 to 2023. Several AI algorithms were evaluated on different orthognathic surgical procedures, revealing the high predictive accuracy of AI models across various facial regions. Conclusions AI demonstrates significant potential for enhancing the precision of facial outcome predictions following orthognathic surgery. However, despite the promising results, limitations such as small sample sizes and a lack of external validation were noted. Further research with larger, more diverse datasets and standardized validation methods is essential for optimizing AI's clinical utility. Key words:Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Orthognathic Surgery, Facial Soft-tissue Prediction, Predictive Accuracy, Orthodontics.
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Affiliation(s)
- Asim A Almarhoumi
- M.Orth RCSEd. Division of Orthodontics, Department of Preventive Dental Sciences, College of Dentistry and Dental Hospital at Taibah University, Madinah, Saudi Arabia
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Lee J, Kim D, Xu X, Kuang T, Gateno J, Yan P. Predicting Optimal Patient-Specific Postoperative Facial Landmarks for Patients with Craniomaxillofacial Deformities. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.13.23299919. [PMID: 38187692 PMCID: PMC10767768 DOI: 10.1101/2023.12.13.23299919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Orthognathic surgery traditionally focuses on correcting skeletal abnormalities and malocclusion, with the expectation that an optimal facial appearance will naturally follow. However, this skeletal-driven approach can lead to undesirable facial aesthetics and residual asymmetry. To address these issues, a soft-tissue-driven planning method has been proposed. This innovative method bases bone movement estimates on the targeted ideal facial appearance, thus increasing the surgical plan's accuracy and effectiveness. This study explores the initial phase of implementing a soft-tissue-driven approach, simulating the patient's optimal facial look by repositioning deformed facial landmarks to an ideal state. The algorithm incorporates symmetrization and weighted optimization strategies, aligning projected optimal landmarks with standard cephalometric values for both facial symmetry and form, which are integral to facial aesthetics in orthognathic surgery. It also includes regularization to preserve the patient's original facial characteristics. Validated using retrospective analysis of data from both preoperative patients and normal subjects, this approach effectively achieves not only facial symmetry, particularly in the lower face, but also a more natural and normalized facial form. This novel approach, aligning with soft-tissue-driven planning principles, shows promise in surpassing traditional methods, potentially leading to enhanced facial outcomes and patient satisfaction in orthognathic surgery.
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Affiliation(s)
- Jungwook Lee
- Department of Biomedical Engineering and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Daeseung Kim
- Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, TX, 77030, USA
| | - Xuanang Xu
- Department of Biomedical Engineering and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Tianshu Kuang
- Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, TX, 77030, USA
| | - Jaime Gateno
- Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, TX, 77030, USA
- Department of Surgery (Oral and Maxillofacial Surgery), Weill Medical College, Cornell University, New York, NY, 10021, USA
| | - Pingkun Yan
- Department of Biomedical Engineering and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
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Ma L, Xiao D, Kim D, Lian C, Kuang T, Liu Q, Deng H, Yang E, Liebschner MAK, Gateno J, Xia JJ, Yap PT. Simulation of Postoperative Facial Appearances via Geometric Deep Learning for Efficient Orthognathic Surgical Planning. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:336-345. [PMID: 35657829 PMCID: PMC10037541 DOI: 10.1109/tmi.2022.3180078] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
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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Evaluation of soft tissue prediction accuracy for orthognathic surgery with skeletal class III malocclusion using maxillofacial regional aesthetic units. Clin Oral Investig 2023; 27:173-182. [PMID: 36161529 DOI: 10.1007/s00784-022-04705-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 08/29/2022] [Indexed: 01/28/2023]
Abstract
OBJECTIVES This study aimed to evaluate the soft tissue prediction accuracy of patients undergoing orthognathic surgery to correct skeletal class III malocclusion using maxillofacial regional aesthetic units. MATERIALS AND METHODS Pre- and postoperative cone-beam computed tomography (CBCT) and 3D facial scans were taken for 58 patients who had undergone orthognathic surgery. The preoperative 3D facial scan was integrated with the preoperative CBCT using ProPlan CMF software. The software simulated the surgery and generated postoperative soft tissue prediction. The simulated 3D facial scan was then compared with the actual 3D facial scan obtained at least 6 months after the surgery by the maxillofacial regional aesthetic units and the facial soft tissue landmark points. RESULTS The anatomical regions of the upper lip, lower lip, chin, right external buccal and left external buccal prediction were above 2.0 mm. As for the soft tissue landmarks, at chl, chr, ls, stm and li, the position of predicted scan was higher than that of the actual postoperative scan. CONCLUSIONS The accuracy of 3D soft tissue predictions using ProPlan CMF software in Skeletal III patients was clinically satisfactory according to maxillofacial regional aesthetic units combined with facial soft tissue landmark points. However, the accuracy of prediction still needed improvement in some areas. CLINICAL RELEVANCE The accuracy of soft tissue prediction can be analyzed more clearly through maxillofacial regional aesthetic units so that clinicians have a deeper understanding of the use of the software to predict soft tissue change after orthognathic surgery.
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A Quantitative and Qualitative Clinical Validation of Soft Tissue Simulation for Orthognathic Surgery Planning. J Pers Med 2022; 12:jpm12091460. [PMID: 36143245 PMCID: PMC9503761 DOI: 10.3390/jpm12091460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 08/29/2022] [Accepted: 08/31/2022] [Indexed: 11/30/2022] Open
Abstract
The purpose of this study was to perform a quantitative and qualitative validation of a soft tissue simulation pipeline for orthognathic surgery planning, necessary for clinical use. Simulation results were retrospectively obtained in 10 patients who underwent orthognathic surgery. Quantitatively, error was measured at 9 anatomical landmarks for each patient and different types of comparative analysis were performed considering two mesh resolutions, clinically accepted error, simulation time and error measured by means of percentage of the whole surface. Qualitatively, evaluation and binary questions were asked to two surgeons, both before and after seeing the actual surgical outcome, and their answers were compared. Finally, the quantitative and qualitative results were compared to check if these two types of validation are correlated. The quantitative results were accurate, with greater errors corresponding to gonions and lower lip. Qualitatively, surgeons answered similarly mostly and their evaluations improved when seeing the actual outcome of the surgery. The quantitative validation was not correlated to the qualitative validation. In this study, quantitative and qualitative validations were performed and compared, and the need to carry out both types of analysis in validation studies of soft tissue simulation software for orthognathic surgery planning was proved.
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8
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Deep learning for biomechanical modeling of facial tissue deformation in orthognathic surgical planning. Int J Comput Assist Radiol Surg 2022; 17:945-952. [PMID: 35362849 DOI: 10.1007/s11548-022-02596-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 03/08/2022] [Indexed: 11/05/2022]
Abstract
PURPOSE Orthognathic 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. METHODS A 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. RESULTS We 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. CONCLUSION Our 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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Singh GD, Singh M. Virtual Surgical Planning: Modeling from the Present to the Future. J Clin Med 2021; 10:jcm10235655. [PMID: 34884359 PMCID: PMC8658225 DOI: 10.3390/jcm10235655] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 10/19/2021] [Accepted: 11/23/2021] [Indexed: 11/16/2022] Open
Abstract
Virtual surgery planning is a non-invasive procedure, which uses digital clinical data for diagnostic, procedure selection and treatment planning purposes, including the forecast of potential outcomes. The technique begins with 3D data acquisition, using various methods, which may or may not utilize ionizing radiation, such as 3D stereophotogrammetry, 3D cone-beam CT scans, etc. Regardless of the imaging technique selected, landmark selection, whether it is manual or automated, is the key to transforming clinical data into objects that can be interrogated in virtual space. As a prerequisite, the data require alignment and correspondence such that pre- and post-operative configurations can be compared in real and statistical shape space. In addition, these data permit predictive modeling, using either model-based, data-based or hybrid modeling. These approaches provide perspectives for the development of customized surgical procedures and medical devices with accuracy, precision and intelligence. Therefore, this review briefly summarizes the current state of virtual surgery planning.
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Affiliation(s)
- G. Dave Singh
- Virtual Craniofacial Laboratory, Stanford University, Stanford, CA 94301, USA
- Correspondence: ; Tel.: +1-720-924-9929
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10
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Soft-Tissue Simulation for Computational Planning of Orthognathic Surgery. J Pers Med 2021; 11:jpm11100982. [PMID: 34683123 PMCID: PMC8540582 DOI: 10.3390/jpm11100982] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 09/21/2021] [Accepted: 09/25/2021] [Indexed: 11/23/2022] Open
Abstract
Simulation technologies offer interesting opportunities for computer planning of orthognathic surgery. However, the methods used to date require tedious set up of simulation meshes based on patient imaging data, and they rely on complex simulation models that require long computations. In this work, we propose a modeling and simulation methodology that addresses model set up and runtime simulation in a holistic manner. We pay special attention to modeling the coupling of rigid-bone and soft-tissue components of the facial model, such that the resulting model is computationally simple yet accurate. The proposed simulation methodology has been evaluated on a cohort of 10 patients of orthognathic surgery, comparing quantitatively simulation results to post-operative scans. The results suggest that the proposed simulation methods admit the use of coarse simulation meshes, with planning computation times of less than 10 seconds in most cases, and with clinically viable accuracy.
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Ma L, Kim D, Lian C, Xiao D, Kuang T, Liu Q, Lang Y, Deng HH, Gateno J, Wu Y, Yang E, Liebschner MAK, Xia JJ, Yap PT. Deep Simulation of Facial Appearance Changes Following Craniomaxillofacial Bony Movements in Orthognathic Surgical Planning. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2021; 12904:459-468. [PMID: 34966912 PMCID: PMC8713535 DOI: 10.1007/978-3-030-87202-1_44] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Facial appearance changes with the movements of bony segments in orthognathic surgery of patients with craniomaxillofacial (CMF) deformities. Conventional bio-mechanical methods, such as finite element modeling (FEM), for simulating such changes, are labor intensive and computationally expensive, preventing them from being used in clinical settings. To overcome these limitations, we propose a deep learning framework to predict post-operative facial changes. Specifically, FC-Net, a facial appearance change simulation network, is developed to predict the point displacement vectors associated with a facial point cloud. FC-Net learns the point displacements of a pre-operative facial point cloud from the bony movement vectors between pre-operative and simulated post-operative bony models. FC-Net is a weakly-supervised point displacement network trained using paired data with strict point-to-point correspondence. To preserve the topology of the facial model during point transform, we employ a local-point-transform loss to constrain the local movements of points. Experimental results on real patient data reveal that the proposed framework can predict post-operative facial appearance changes remarkably faster than a state-of-the-art FEM method with comparable prediction accuracy.
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Affiliation(s)
- Lei Ma
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Daeseung Kim
- Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, TX, USA
| | - Chunfeng Lian
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Deqiang Xiao
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Tianshu Kuang
- Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, TX, USA
| | - Qin Liu
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yankun Lang
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Hannah H Deng
- Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, TX, USA
| | - Jaime Gateno
- Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, TX, USA
- Department of Surgery (Oral and Maxillofacial Surgery), Weill Medical College, Cornell University, Ithaca, NY, USA
| | - Ye Wu
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Erkun Yang
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | | | - James J Xia
- Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, TX, USA
- Department of Surgery (Oral and Maxillofacial Surgery), Weill Medical College, Cornell University, Ithaca, NY, USA
| | - Pew-Thian Yap
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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12
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Kim D, Kuang T, Rodrigues YL, Gateno J, Shen SGF, Wang X, Stein K, Deng HH, Liebschner MAK, Xia JJ. A novel incremental simulation of facial changes following orthognathic surgery using FEM with realistic lip sliding effect. Med Image Anal 2021; 72:102095. [PMID: 34090256 DOI: 10.1016/j.media.2021.102095] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 04/16/2021] [Accepted: 04/22/2021] [Indexed: 11/16/2022]
Abstract
Accurate prediction of facial soft-tissue changes following orthognathic surgery is crucial for surgical outcome improvement. We developed a novel incremental simulation approach using finite element method (FEM) with a realistic lip sliding effect to improve the prediction accuracy in the lip region. First, a lip-detailed mesh is generated based on accurately digitized lip surface points. Second, an improved facial soft-tissue change simulation method is developed by applying a lip sliding effect along with the mucosa sliding effect. Finally, the orthognathic surgery initiated soft-tissue change is simulated incrementally to facilitate a natural transition of the facial change and improve the effectiveness of the sliding effects. Our method was quantitatively validated using 35 retrospective clinical data sets by comparing it to the traditional FEM simulation method and the FEM simulation method with mucosa sliding effect only. The surface deviation error of our method showed significant improvement in the upper and lower lips over the other two prior methods. In addition, the evaluation results using our lip-shape analysis, which reflects clinician's qualitative evaluation, also proved significant improvement of the lip prediction accuracy of our method for the lower lip and both upper and lower lips as a whole compared to the other two methods. In conclusion, the prediction accuracy in the clinically critical region, i.e., the lips, significantly improved after applying incremental simulation with realistic lip sliding effect compared with the FEM simulation methods without the lip sliding effect.
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Affiliation(s)
- Daeseung Kim
- Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, 6560 Fannin St, Houston, TX 77030, USA
| | - Tianshu Kuang
- Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, 6560 Fannin St, Houston, TX 77030, USA
| | - Yriu L Rodrigues
- Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, 6560 Fannin St, Houston, TX 77030, USA
| | - Jaime Gateno
- Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, 6560 Fannin St, Houston, TX 77030, USA; Department of Surgery (Oral and Maxillofacial Surgery), Weill Medical College, Cornell University, 407 E 61st St, New York, NY 10065, USA
| | - Steve G F Shen
- Department of Oral and Craniomaxillofacial Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University College of Medicine, 639 Zhi-Zao-Ju Road, Shanghai 200011, China
| | - Xudong Wang
- Department of Oral and Craniomaxillofacial Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University College of Medicine, 639 Zhi-Zao-Ju Road, Shanghai 200011, China
| | - Kirhyn Stein
- Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, 6560 Fannin St, Houston, TX 77030, USA
| | - Hannah H Deng
- Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, 6560 Fannin St, Houston, TX 77030, USA
| | - Michael A K Liebschner
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA.
| | - James J Xia
- Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, 6560 Fannin St, Houston, TX 77030, USA; Department of Surgery (Oral and Maxillofacial Surgery), Weill Medical College, Cornell University, 407 E 61st St, New York, NY 10065, USA.
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