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Fang X, Deng HH, Kuang T, Xu X, Lee J, Gateno J, Yan P. Patient-specific reference model estimation for orthognathic surgical planning. Int J Comput Assist Radiol Surg 2024; 19:1439-1447. [PMID: 38869779 DOI: 10.1007/s11548-024-03123-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Accepted: 03/22/2024] [Indexed: 06/14/2024]
Abstract
PURPOSE Accurate estimation of reference bony shape models is fundamental for orthognathic surgical planning. Existing methods to derive this model are of two types: one determines the reference model by estimating the deformation field to correct the patient's deformed jaw, often introducing distortions in the predicted reference model; The other derives the reference model using a linear combination of their landmarks/vertices but overlooks the intricate nonlinear relationship between the subjects, compromising the model's precision and quality. METHODS We have created a self-supervised learning framework to estimate the reference model. The core of this framework is a deep query network, which estimates the similarity scores between the patient's midface and those of the normal subjects in a high-dimensional space. Subsequently, it aggregates high-dimensional features of these subjects and projects these features back to 3D structures, ultimately achieving a patient-specific reference model. RESULTS Our approach was trained using a dataset of 51 normal subjects and tested on 30 patient subjects to estimate their reference models. Performance assessment against the actual post-operative bone revealed a mean Chamfer distance error of 2.25 mm and an average surface distance error of 2.30 mm across the patient subjects. CONCLUSION Our proposed method emphasizes the correlation between the patients and the normal subjects in a high-dimensional space, facilitating the generation of the patient-specific reference model. Both qualitative and quantitative results demonstrate its superiority over current state-of-the-art methods in reference model estimation.
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Affiliation(s)
- Xi Fang
- Department of Biomedical Engineering and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA
| | - Hannah H Deng
- Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, TX, 77030, USA
| | - Tianshu Kuang
- 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
| | - Jungwook Lee
- Department of Biomedical Engineering and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, 12180, 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, NewYork, 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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Miloro M, Han MD, Kwon TG, Xi T, Borba AM, Reyneke JP, Caminiti M. Predicting the Future Focus of Orthognathic Surgery: Outcome-Driven Planning and Treatment With Function, Esthetics, and Occlusion as Key Indicators. J Oral Maxillofac Surg 2024:S0278-2391(24)00576-7. [PMID: 38981579 DOI: 10.1016/j.joms.2024.06.168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Revised: 06/13/2024] [Accepted: 06/14/2024] [Indexed: 07/11/2024]
Abstract
As an international group of orthognathic surgeons, we believe the next big thing in orthognathic surgery will be a clinical and research focus on patient-oriented outcomes and improved quality of life. We expect to see advances in diagnosis and treatment planning, materials development, and patient management.
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Affiliation(s)
- Michael Miloro
- Daniel M. Laskin Professor and Department Head, Oral and Maxillofacial Surgery, University of Illinois Chicago, Chicago, IL
| | - Michael D Han
- Associate Professor and Program Director, Oral and Maxillofacial Surgery, University of Illinois Chicago, Chicago, IL
| | - Tae-Geon Kwon
- Professor, Department of Oral & Maxillofacial Surgery, School of Dentistry, Kyungpook National University, Daegu, Korea
| | - Tong Xi
- Associate Professor and Research Coordinator, Oral and Maxillofacial Surgery, Radboud University Medical Center, Nijmegen, Netherlands; Adjunct Associate Professor, Faculty of Health, Clinical Institute, University of Southern Denmark, Odense, Denmark
| | - Alexandre M Borba
- Program Director, Research Program in Integrated Dental Sciences Department, Faculty of Dentistry of the University of Cuiabá, Cuiabá, MT, Brazil; Staff, Department of Oral and Maxillofacial Surgery, General Hospital of Cuiabá, Cuiabá, MT, Brazil
| | - Johan P Reyneke
- Professor, Director, Faculty of Health Sciences, Department of Oral and Maxillofacial Surgery, University of Western Cape, The Centre for Orthognathic Surgery, Mediclinic, Cape Town, South Africa
| | - Marco Caminiti
- Associate Professor, Head and Program Director, Oral and Maxillofacial Surgery, University of Toronto, Toronto, Canada.
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3
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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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4
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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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Fang X, Kim D, Xu X, Kuang T, Lampen N, Lee J, Deng HH, Liebschner MAK, Xia JJ, Gateno J, Yan P. Correspondence attention for facial appearance simulation. Med Image Anal 2024; 93:103094. [PMID: 38306802 PMCID: PMC11265218 DOI: 10.1016/j.media.2024.103094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 12/02/2023] [Accepted: 01/22/2024] [Indexed: 02/04/2024]
Abstract
In orthognathic surgical planning for patients with jaw deformities, it is crucial to accurately simulate the changes in facial appearance that follow the bony movement. Compared with the traditional biomechanics-based methods like the finite-element method (FEM), which are both labor-intensive and computationally inefficient, deep learning-based methods offer an efficient and robust modeling alternative. However, current methods do not account for the physical relationship between facial soft tissue and bony structure, causing them to fall short in accuracy compared to FEM. In this work, we propose an Attentive Correspondence assisted Movement Transformation network (ACMT-Net) to predict facial changes by correlating facial soft tissue changes with bony movement through a point-to-point attentive correspondence matrix. To ensure efficient training, we also introduce a contrastive loss for self-supervised pre-training of the ACMT-Net with a k-Nearest Neighbors (k-NN) based clustering. Experimental results on patients with jaw deformities show that our proposed solution can achieve significantly improved computational efficiency over the state-of-the-art FEM-based method with comparable facial change prediction accuracy.
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Affiliation(s)
- Xi Fang
- 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
| | - Nathan Lampen
- Department of Biomedical Engineering and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Jungwook Lee
- Department of Biomedical Engineering and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Hannah H Deng
- Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, TX 77030, USA
| | | | - James J Xia
- Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, TX 77030, USA; Weill Medical College, Cornell University, New York, NY, 10021, USA
| | - Jaime Gateno
- Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, TX 77030, USA; 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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Zammit D, Ettinger RE, Sanati-Mehrizy P, Susarla SM. Current Trends in Orthognathic Surgery. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:2100. [PMID: 38138203 PMCID: PMC10744503 DOI: 10.3390/medicina59122100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 11/14/2023] [Accepted: 11/24/2023] [Indexed: 12/24/2023]
Abstract
Orthognathic surgery has evolved significantly over the past century. Osteotomies of the midface and mandible are contemporaneously used to perform independent or coordinated movements to address functional and aesthetic problems. Specific advances in the past twenty years include increasing fidelity with computer-assisted planning, the use of patient-specific fixation, expanding indications for management of upper airway obstruction, and shifts in orthodontic-surgical paradigms. This review article serves to highlight the contemporary practice of orthognathic surgery.
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Affiliation(s)
- Domenick Zammit
- Department of Pediatric Surgery, Division of Plastic Surgery, McGill University Health Center, Montreal Children’s Hospital, Montreal, QC H3Z 1X3, Canada;
| | - Russell E. Ettinger
- Department of Surgery, Division of Plastic Surgery, University of Washington, Seattle, WA 98105, USA
- Craniofacial Center, Seattle Children’s Hospital, Seattle, WA 98105, USA
| | - Paymon Sanati-Mehrizy
- Department of Plastic Surgery, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA;
| | - Srinivas M. Susarla
- Department of Surgery, Division of Plastic Surgery, University of Washington, Seattle, WA 98105, USA
- Craniofacial Center, Seattle Children’s Hospital, Seattle, WA 98105, USA
- Department of Oral and Maxillofacial Surgery, University of Washington, Seattle, WA 98195, USA
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Ruggiero F, Borghi A, Bevini M, Badiali G, Lunari O, Dunaway D, Marchetti C. Soft tissue prediction in orthognathic surgery: Improving accuracy by means of anatomical details. PLoS One 2023; 18:e0294640. [PMID: 38011187 PMCID: PMC10681161 DOI: 10.1371/journal.pone.0294640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 11/06/2023] [Indexed: 11/29/2023] Open
Abstract
Three-dimensional virtual simulation of orthognathic surgery is now a well-established method in maxillo-facial surgery. The commercial software packages are still burdened by a consistent imprecision on soft tissue predictions. In this study, the authors produced an anatomically detailed patient specific numerical model for simulation of soft tissue changes in orthognathic surgery. Eight patients were prospectively enrolled. Each patient underwent CBCT and planar x-rays prior to surgery and in addition received an MRI scan. Postoperative soft-tissue change was simulated using Finite Element Modeling (FEM) relying on a patient-specific 3D models generated combining data from preoperative CBCT (hard tissue) scans and MRI scans (muscles and skin). An initial simulation was performed assuming that all the muscles and the other soft tissue had the same material properties (Homogeneous Model). This model was compared with the postoperative CBCT 3D simulation for validation purpose. Design of experiments (DoE) was used to assess the effect of the presence of the muscles considered and of their variation in stiffness. The effect of single muscles was evaluated in specific areas of the midface. The quantitative distance error between the homogeneous model and actual patient surfaces for the midface area was 0.55 mm, standard deviation 2.9 mm. In our experience, including muscles in the numerical simulation of orthognathic surgery, brought an improvement in the quality of the simulation obtained.
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Affiliation(s)
| | - Alessandro Borghi
- Department of Engineering, Durham University, Durham, United Kingdom
| | - Mirko Bevini
- Oral and Maxillofacial Surgery Unit, IRCCS AOU di Bologna, Bologna, Italy
| | - Giovanni Badiali
- DIBINEM, Alma Mater Studiorum University of Bologna, Bologna, Italy
- Oral and Maxillofacial Surgery Unit, IRCCS AOU di Bologna, Bologna, Italy
| | - Ottavia Lunari
- Oral and Maxillofacial Surgery Unit, IRCCS AOU di Bologna, Bologna, Italy
| | - David Dunaway
- Craniofacial Unit, Great Ormond Street Hospital, London, United Kingdom
| | - Claudio Marchetti
- DIBINEM, Alma Mater Studiorum University of Bologna, Bologna, Italy
- Oral and Maxillofacial Surgery Unit, IRCCS AOU di Bologna, Bologna, Italy
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Cai J, Deng Y, Min Z, Zhang Y, Zhao Z, Jing D. Revealing the representative facial traits of different sagittal skeletal types: decipher what artificial intelligence can see by Grad-CAM. J Dent 2023; 138:104701. [PMID: 37717687 DOI: 10.1016/j.jdent.2023.104701] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 09/10/2023] [Accepted: 09/12/2023] [Indexed: 09/19/2023] Open
Abstract
OBJECTIVES Aesthetic improvement is a significant concern in dental therapy. While orthodontic treatment primarily targets hard tissue, the impact on soft tissue and the extent of these changes remains empirical. This study aims to unveil the intricate relationship between facial soft tissue and skeletal types using artificial intelligence (AI) analysis. METHODS First, we collected a dataset of 1044 3-side-photographs and categorized them based on cephalometric measurements. After pre-processing and data augmentation, samples were fed to two independent models (Sfa, Res model) for training and testing. After validating that the Sfa model could accurately recognize the skeletal types based merely on photographs, Grad-CAM algorithm was utilized for model decipherment. Verification of the vital traits were carried out by facial adjustment simulation. RESULTS The Sfa model demonstrated superior accuracy (0.9293) in identifying skeletal types based solely on soft tissue, compared to the Res model (0.8395) and even trained orthodontists (0.764), testifying our hypothesis that AI could be more capable of processing imperceptible cues compared to mankind. Intriguingly, Grad-CAM revealed that cheek volume, forehead, chin and nasolabial traits could be representative features of each type, exceeding the traditional knowledge which merely concerns mandible and chin. CONCLUSION By constructing a deep learning model as a classifier and then decipher it with Grad-CAM, we revealed the subtle and unnoticed cues associating skeletal and soft tissue, as well as provided a novel approach that could aid practitioners in devising tailored treatment plans for enhanced esthetic outcomes. CLINICAL SIGNIFICANCE The proposed AI methods offer valuable assistance to practitioners in identifying uncoordinated facial traits that may detract from a patient's attractiveness. By incorporating these insights into customized treatment plans, dental therapy can maximize esthetic benefits for individual patient.
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Affiliation(s)
- Jingyi Cai
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, No.14, 3rd Section, South Renmin Road, Chengdu, Sichuan 610041 China
| | - Yudi Deng
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, No.14, 3rd Section, South Renmin Road, Chengdu, Sichuan 610041 China
| | - Ziyang Min
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, No.14, 3rd Section, South Renmin Road, Chengdu, Sichuan 610041 China
| | - Yiyi Zhang
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, No.14, 3rd Section, South Renmin Road, Chengdu, Sichuan 610041 China
| | - Zhihe Zhao
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, No.14, 3rd Section, South Renmin Road, Chengdu, Sichuan 610041 China.
| | - Dian Jing
- Department of Orthodontics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai Jiao Tong University, No.639, Zhizaoju Road, Huangpu District, Shanghai 200011, China.
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Cheng M, Zhang X, Wang J, Yang Y, Li M, Zhao H, Huang J, Zhang C, Qian D, Yu H. Prediction of orthognathic surgery plan from 3D cephalometric analysis via deep learning. BMC Oral Health 2023; 23:161. [PMID: 36934241 PMCID: PMC10024836 DOI: 10.1186/s12903-023-02844-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 02/27/2023] [Indexed: 03/20/2023] Open
Abstract
BACKGROUND Preoperative planning of orthognathic surgery is indispensable for achieving ideal surgical outcome regarding the occlusion and jaws' position. However, orthognathic surgery planning is sophisticated and highly experience-dependent, which requires comprehensive consideration of facial morphology and occlusal function. This study aimed to investigate a robust and automatic method based on deep learning to predict reposition vectors of jawbones in orthognathic surgery plan. METHODS A regression neural network named VSP transformer was developed based on Transformer architecture. Firstly, 3D cephalometric analysis was employed to quantify skeletal-facial morphology as input features. Next, input features were weighted using pretrained results to minimize bias resulted from multicollinearity. Through encoder-decoder blocks, ten landmark-based reposition vectors of jawbones were predicted. Permutation importance (PI) method was used to calculate contributions of each feature to final prediction to reveal interpretability of the proposed model. RESULTS VSP transformer model was developed with 383 samples and clinically tested with 49 prospectively collected samples. Our proposed model outperformed other four classic regression models in prediction accuracy. Mean absolute errors (MAE) of prediction were 1.41 mm in validation set and 1.34 mm in clinical test set. The interpretability results of the model were highly consistent with clinical knowledge and experience. CONCLUSIONS The developed model can predict reposition vectors of orthognathic surgery plan with high accuracy and good clinically practical-effectiveness. Moreover, the model was proved reliable because of its good interpretability.
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Affiliation(s)
- Mengjia Cheng
- Department of Oral and Cranio-Maxillofacial Surgery, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China
- National Center for Stomatology & National Clinical Research Center for Oral Diseases, Shanghai, 200011, China
- Shanghai Key Laboratory of Stomatology &, Shanghai Research Institute of Stomatology, Shanghai, 200011, China
| | - Xu Zhang
- Mechanical College, Shanghai Dianji University, Shanghai, 201306, China
| | - Jun Wang
- School of Computer & Computing Science, Hangzhou City University, Hangzhou, 310000, China
| | - Yang Yang
- Shanghai Lanhui Medical Technology Co., Ltd, Shanghai, 200333, China
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Meng Li
- Department of Oral and Cranio-Maxillofacial Surgery, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China
- National Center for Stomatology & National Clinical Research Center for Oral Diseases, Shanghai, 200011, China
- Shanghai Key Laboratory of Stomatology &, Shanghai Research Institute of Stomatology, Shanghai, 200011, China
| | - Hanjiang Zhao
- Department of Oral and Cranio-Maxillofacial Surgery, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China
- National Center for Stomatology & National Clinical Research Center for Oral Diseases, Shanghai, 200011, China
- Shanghai Key Laboratory of Stomatology &, Shanghai Research Institute of Stomatology, Shanghai, 200011, China
| | - Jingyang Huang
- Department of Oral and Cranio-Maxillofacial Surgery, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China
- National Center for Stomatology & National Clinical Research Center for Oral Diseases, Shanghai, 200011, China
- Shanghai Key Laboratory of Stomatology &, Shanghai Research Institute of Stomatology, Shanghai, 200011, China
| | - Chenglong Zhang
- Department of Oral and Cranio-Maxillofacial Surgery, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China
- National Center for Stomatology & National Clinical Research Center for Oral Diseases, Shanghai, 200011, China
- Shanghai Key Laboratory of Stomatology &, Shanghai Research Institute of Stomatology, Shanghai, 200011, China
| | - Dahong Qian
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China.
| | - Hongbo Yu
- Department of Oral and Cranio-Maxillofacial Surgery, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China.
- National Center for Stomatology & National Clinical Research Center for Oral Diseases, Shanghai, 200011, China.
- Shanghai Key Laboratory of Stomatology &, Shanghai Research Institute of Stomatology, Shanghai, 200011, China.
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10
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Guo N, Tian J, Wang L, Sun K, Mi L, Ming H, Zhe Z, Sun F. 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. Front Bioeng Biotechnol 2022; 10:1016598. [PMID: 36246357 PMCID: PMC9561816 DOI: 10.3389/fbioe.2022.1016598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 09/14/2022] [Indexed: 11/16/2022] Open
Abstract
Although intelligent technologies has facilitated the development of precise orthopaedic, simple internal fixation, ligament reconstruction or arthroplasty can only relieve pain of patients in short-term. To achieve the best recover of musculoskeletal injuries, three bottlenecks must be broken through, which includes scientific path planning, bioactive implants and personalized surgical channels building. As scientific surgical path can be planned and built by through AI technology, 4D printing technology can make more bioactive implants be manufactured, and variable structures can establish personalized channels precisely, it is possible to achieve satisfied and effective musculoskeletal injury recovery with the progress of multi-layer intelligent technologies (MLIT).
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Affiliation(s)
- Na Guo
- Department of Computer Science and Technology, Tsinghua University, Beijing, China
- Institute of Precision Medicine, Tsinghua University, Beijing, China
| | - Jiawen Tian
- Department of Computer Science and Technology, Tsinghua University, Beijing, China
- Institute of Precision Medicine, Tsinghua University, Beijing, China
| | - Litao Wang
- College of Engineering, China Agricultural University, Beijing, China
| | - Kai Sun
- Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Lixin Mi
- Musculoskeletal Department, Beijing Rehabilitation Hospital, Beijing, China
| | - Hao Ming
- Orthopaedics, Chinese PLA General Hospital, Beijing, China
| | - Zhao Zhe
- Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Fuchun Sun
- Department of Computer Science and Technology, Tsinghua University, Beijing, China
- Institute of Precision Medicine, Tsinghua University, Beijing, China
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