Correspondence attention for facial appearance simulation.

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.

Accuracy of Surgical Outcome Using Computer-Aided Surgical Simulation in Fronto-Orbital Advancement for Craniosynostosis: A Pilot Study.

BACKGROUND AND OBJECTIVE: Computer-aided surgical simulation (CASS) can be used to virtually plan ideal outcomes of craniosynostosis surgery. Our purpose was to create a workflow analyzing the accuracy of surgical outcomes relative to virtually planned fronto-orbital advancement (FOA). METHODS: Patients who underwent FOA using CASS between October 1, 2017, and February 28, 2022, at our center and had postoperative computed tomography within 6 months of surgery were included. Virtual 3-dimensional (3D) models were created and coregistered using each patient's preoperative and postoperative computed tomography data. Three points on each bony segment were used to define the object in 3D space. Each planned bony segment was manipulated to match the actual postoperative outcome. The change in position of the 3D object was measured in translational (X, Y, Z) and rotational (roll, pitch, yaw) aspects to represent differences between planned and actual postoperative positions. The difference in the translational position of several bony landmarks was also recorded. Wilcoxon signed-rank tests were performed to measure significance of these differences from the ideal value of 0, which would indicate no difference between preoperative plan and postoperative outcome. RESULTS: Data for 63 bony segments were analyzed from 8 patients who met the inclusion criteria. Median differences between planned and actual outcomes of the segment groups ranged from -0.3 to -1.3 mm in the X plane; 1.4 to 5.6 mm in the Y plane; 0.9 to 2.7 mm in the Z plane; -1.2° to -4.5° in pitch; -0.1° to 1.0° in roll; and -2.8° to 1.0° in yaw. No significant difference from 0 was found in 21 of 24 segment region/side combinations. Translational differences of bony landmarks ranged from -2.7 to 3.6 mm. CONCLUSION: A high degree of accuracy was observed relative to the CASS plan. Virtual analysis of surgical accuracy in FOA using CASS was feasible.

Simulation of Postoperative Facial Appearances via Geometric Deep Learning for Efficient Orthognathic Surgical Planning.

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.

Midsagittal Plane First: Building a Strong Facial Reference Frame for Computer-Aided Surgical Simulation.

PURPOSE: A facial reference frame is a 3-dimensional Cartesian coordinate system that includes 3 perpendicular planes: midsagittal, axial, and coronal. The order in which one defines the planes matters. The purposes of this study are to determine the following: 1) what sequence (axial-midsagittal-coronal vs midsagittal-axial-coronal) produced more appropriate reference frames and 2) whether orbital or auricular dystopia influenced the outcomes. METHODS: This study is an ambispective cross-sectional study. Fifty-four subjects with facial asymmetry were included. The facial reference frames of each subject (outcome variable) were constructed using 2 methods (independent variable): axial plane first and midsagittal plane first. Two board-certified orthodontists together blindly evaluated the results using a 3-point categorical scale based on their careful inspection and expert intuition. The covariant for stratification was the existence of orbital or auricular dystopia. Finally, Wilcoxon signed rank tests were performed. RESULTS: The facial reference frames defined by the midsagittal plane first method was statistically significantly different from ones defined by the axial plane first method (P = .001). Using the midsagittal plane first method, the reference frames were more appropriately defined in 22 (40.7%) subjects, equivalent in 26 (48.1%) and less appropriately defined in 6 (11.1%). After stratified by orbital or auricular dystopia, the results also showed that the reference frame computed using midsagittal plane first method was statistically significantly more appropriate in both subject groups regardless of the existence of orbital or auricular dystopia (27 with orbital or auricular dystopia and 27 without, both P < .05). CONCLUSIONS: The midsagittal plane first sequence improves the facial reference frames compared with the traditional axial plane first approach. However, regardless of the sequence used, clinicians need to judge the correctness of the reference frame before diagnosis or surgical planning.

DLLNet: An Attention-Based Deep Learning Method for Dental Landmark Localization on High-Resolution 3D Digital Dental Models.

Dental landmark localization is a fundamental step to analyzing dental models in the planning of orthodontic or orthognathic surgery. However, current clinical practices require clinicians to manually digitize more than 60 landmarks on 3D dental models. Automatic methods to detect landmarks can release clinicians from the tedious labor of manual annotation and improve localization accuracy. Most existing landmark detection methods fail to capture local geometric contexts, causing large errors and misdetections. We propose an end-to-end learning framework to automatically localize 68 landmarks on high-resolution dental surfaces. Our network hierarchically extracts multi-scale local contextual features along two paths: a landmark localization path and a landmark area-of-interest segmentation path. Higher-level features are learned by combining local-to-global features from the two paths by feature fusion to predict the landmark heatmap and the landmark area segmentation map. An attention mechanism is then applied to the two maps to refine the landmark position. We evaluated our framework on a real-patient dataset consisting of 77 high-resolution dental surfaces. Our approach achieves an average localization error of 0.42 mm, significantly outperforming related start-of-the-art methods.

Deep Simulation of Facial Appearance Changes Following Craniomaxillofacial Bony Movements in Orthognathic Surgical Planning.

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.

Unsupervised learning of reference bony shapes for orthognathic surgical planning with a surface deformation network.

PURPOSE: The purpose of this study was to reduce the experience dependence during the orthognathic surgical planning that involves virtually simulating the corrective procedure for jaw deformities. METHODS: We introduce a geometric deep learning framework for generating reference facial bone shape models for objective guidance in surgical planning. First, we propose a surface deformation network to warp a patient's deformed bone to a set of normal bones for generating a dictionary of patient-specific normal bony shapes. Subsequently, sparse representation learning is employed to estimate a reference shape model based on the dictionary. RESULTS: We evaluated our method on a clinical dataset containing 24 patients, and compared it with a state-of-the-art method that relies on landmark-based sparse representation. Our method yields significantly higher accuracy than the competing method for estimating normal jaws and maintains the midfaces of patients' facial bones as well as the conventional way. CONCLUSIONS: Experimental results indicate that our method generates accurate shape models that meet clinical standards.

A novel incremental simulation of facial changes following orthognathic surgery using FEM with realistic lip sliding effect.

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.

Estimating Reference Bony Shape Models for Orthognathic Surgical Planning Using 3D Point-Cloud Deep Learning.

Orthognathic surgical outcomes rely heavily on the quality of surgical planning. Automatic estimation of a reference facial bone shape significantly reduces experience-dependent variability and improves planning accuracy and efficiency. We propose an end-to-end deep learning framework to estimate patient-specific reference bony shape models for patients with orthognathic deformities. Specifically, we apply a point-cloud network to learn a vertex-wise deformation field from a patient's deformed bony shape, represented as a point cloud. The estimated deformation field is then used to correct the deformed bony shape to output a patient-specific reference bony surface model. To train our network effectively, we introduce a simulation strategy to synthesize deformed bones from any given normal bone, producing a relatively large and diverse dataset of shapes for training. Our method was evaluated using both synthetic and real patient data. Experimental results show that our framework estimates realistic reference bony shape models for patients with varying deformities. The performance of our method is consistently better than an existing method and several deep point-cloud networks. Our end-to-end estimation framework based on geometric deep learning shows great potential for improving clinical workflows.

A Better Understanding of Unilateral Condylar Hyperplasia of the Mandible.

PURPOSE: Our current understanding of unilateral condylar hyperplasia (UCH) was put forth by Obwegeser. He hypothesized that UCH is 2 separate conditions: hemimandibular hyperplasia and hemimandibular elongation. This hypothesis was based on the following 3 assumptions: 1) the direction of overgrowth, in UCH, is bimodal-vertical or horizontal, with rare cases growing obliquely; 2) UCH can expand a hemimandible with and without significant condylar enlargement; and 3) there is an association between the condylar expansion and the direction of overgrowth-minimal expansion resulting in horizontal growth and significant enlargement causing vertical displacement. The purpose of this study was to test these assumptions. PATIENTS AND METHODS: We analyzed the computed tomography scans of 40 patients with UCH. First, we used a Silverman Cluster analysis to determine how the direction of overgrowth is distributed in the UCH population. Next, we evaluated the relationship between hemimandibular overgrowth and condylar enlargement to confirm that overgrowth can occur independently of condylar expansion. Finally, we assessed the relationship between the degree of condylar enlargement and the direction of overgrowth to ascertain if condylar expansion determines the direction of growth. RESULTS: Our first investigation demonstrates that the general impression that UCH is bimodal is wrong. The growth vectors in UCH are unimodally distributed, with the vast majority of cases growing diagonally. Our second investigation confirms the observation that UCH can expand a hemimandible with and without significant condylar enlargement. Our last investigation determined that in UCH, there is no association between the degree of condylar expansion and the direction of the overgrowth. CONCLUSIONS: The results of this study disprove the idea that UCH is 2 different conditions: hemimandibular hyperplasia and hemimandibular elongation. It also provides new insights about the pathophysiology of UCH.

Validity of Medical Insurance Guidelines for Orthognathic Surgery.

PURPOSE: The purpose of this study was to assess the validity of the medical insurance guidelines for orthognathic surgery used by the major American medical insurance companies. MATERIALS AND METHODS: This study assessed the validity of the medical insurance guidelines for orthognathic surgery used by Aetna, Anthem Blue Cross Blue Shield (BCBS), Cigna, Humana, and UnitedHealthcare (UHC). To evaluate the validity, we calculated the approval and denial rates of the 5 guidelines when we used them to assess the medical necessity for a control group of carefully selected patients. Patients were included in the control group if they met the criteria of a "prudent provider," crafted for this study. All rejected cases were analyzed to determine the root cause of the denials. The validity of the guidelines was also ascertained by determining their completeness and correctness. RESULTS: The current study proves that no insurance guideline is in agreement with the criteria of a "prudent provider." When applied to carefully chosen patients, the requirements of BCBS, Aetna, Humana, and Cigna produce modest rejection rates of 6 to 12%. UHC is an outlier. Its guideline rejects 86% of patients, a rate about 7 times higher than its peers. Insurance guidelines disqualified patients for 3 different reasons: 1) no significant jaw deformity, 2) no demonstrable health impairment, and 3) the etiology of the condition is not a covered benefit. Additional evaluations demonstrate that the private insurance guidelines are incomplete, and at times, incorrect. CONCLUSIONS: This study shows that the medical insurance guidelines for orthognathic surgery used by the major American medical insurance plans need revision. The most consequential flaw was considering etiology in judging medical necessity. Fortunately, only one company adopted this policy. Moreover, all guidelines have omissions and errors in the way jaw deformity is determined and how health impairment is determined.

An automatic approach to establish clinically desired final dental occlusion for one-piece maxillary orthognathic surgery.

PURPOSE: One critical step in routine orthognathic surgery is to reestablish a desired final dental occlusion. Traditionally, the final occlusion is established by hand articulating stone dental models. To date, there are still no effective solutions to establish the final occlusion in computer-aided surgical simulation. In this study, we consider the most common one-piece maxillary orthognathic surgery and propose a three-stage approach to digitally and automatically establish the desired final dental occlusion. METHODS: The process includes three stages: (1) extraction of points of interest and teeth landmarks from a pair of upper and lower dental models; (2) establishment of Midline-Canine-Molar (M-C-M) relationship following the clinical criteria on these three regions; and (3) fine alignment of upper and lower teeth with maximum contacts without breaking the established M-C-M relationship. Our method has been quantitatively and qualitatively validated using 18 pairs of dental models. RESULTS: Qualitatively, experienced orthodontists assess the algorithm-articulated and hand-articulated occlusions while being blind to the methods used. They agreed that occlusion results of the two methods are equally good. Quantitatively, we measure and compare the distances between selected landmarks on upper and lower teeth for both algorithm-articulated and hand-articulated occlusions. The results showed that there was no statistically significant difference between the algorithm-articulated and hand-articulated occlusions. CONCLUSION: The proposed three-stage automatic dental articulation method is able to articulate the digital dental model to the clinically desired final occlusion accurately and efficiently. It allows doctors to completely eliminate the use of stone dental models in the future.

Clinical Evaluation of Digital Dental Articulation for One-Piece Maxillary Surgery.

PURPOSE: Methods for digital dental alignment are not readily available to automatically articulate the upper and lower jaw models. The purpose of the present study was to assess the accuracy of our newly developed 3-stage automatic digital articulation approach by comparing it with the reference standard of orthodontist-articulated occlusion. MATERIALS AND METHODS: Thirty pairs of stone dental models from double-jaw orthognathic surgery patients who had undergone 1-piece Le Fort I osteotomy were used. Two experienced orthodontists manually articulated the models to their perceived final occlusion for surgery. Each pair of models was then scanned twice-while in the orthodontist-determined occlusion and again with the upper and lower models separated and positioned randomly. The separately scanned models were automatically articulated to the final occlusion using our 3-stage algorithm, resulting in an algorithm-articulated occlusion (experimental group). The models scanned together represented the manually articulated occlusion (control group). A qualitative evaluation was completed using a 3-point categorical scale by the same orthodontists, who were unaware of the methods used to articulate the models. A quantitative evaluation was also completed to determine whether any differences were present in the midline, canine, and molar relationships between the algorithm-determined and manually articulated occlusions using repeated measures analysis of variance (ANOVA). Finally, the mean ± standard deviation values were computed to determine the differences between the 2 methods. RESULTS: The results of the qualitative evaluation revealed that all the algorithm-articulated occlusions were as good as the manually articulated ones. The results of the repeated measures ANOVA found no statistically significant differences between the 2 methods [F(1,28) = 0.03; P = .87]. The mean differences between the 2 methods were all within 0.2 mm. CONCLUSIONS: The results of our study have demonstrated that dental models can be accurately, reliably, and automatically articulated using our 3-stage algorithm approach, meeting the reference standard of orthodontist-articulated occlusion.

Automatic Localization of Landmarks in Craniomaxillofacial CBCT Images Using a Local Attention-Based Graph Convolution Network.

Landmark localization is an important step in quantifying craniomaxillofacial (CMF) deformities and designing treatment plans of reconstructive surgery. However, due to the severity of deformities and defects (partially missing anatomy), it is difficult to automatically and accurately localize a large set of landmarks simultaneously. In this work, we propose two cascaded networks for digitizing 60 anatomical CMF landmarks in cone-beam computed tomography (CBCT) images. The first network is a U-Net that outputs heatmaps for landmark locations and landmark features extracted with a local attention mechanism. The second network is a graph convolution network that takes the features extracted by the first network as input and determines whether each landmark exists via binary classification. We evaluated our approach on 50 sets of CBCT scans of patients with CMF deformities and compared them with state-of-the-art methods. The results indicate that our approach can achieve an average detection error of 1.47mm with a false positive rate of 19%, outperforming related methods.

A New Approach of Predicting Facial Changes following Orthognathic Surgery using Realistic Lip Sliding Effect.

Accurate prediction of facial soft-tissue changes following orthognathic surgery is crucial for improving surgical outcome. However, the accuracy of current prediction methods still requires further improvement in clinically critical regions, especially the lips. We develop a novel incremental simulation approach using finite element method (FEM) with realistic lip sliding effect to improve the prediction accuracy in the area around the lips. First, lip-detailed patient-specific FE mesh is generated based on accurately digitized lip surface landmarks. Second, an improved facial soft-tissue change simulation method is developed by applying a lip sliding effect in addition to the mucosa sliding effect. The soft-tissue change is then simulated incrementally to facilitate a natural transition of the facial change and improve the effectiveness of the sliding effects. A preliminary evaluation of prediction accuracy was conducted using retrospective clinical data. The results showed that there was a significant prediction accuracy improvement in the lip region when the realistic lip sliding effect was applied along with the mucosa sliding effect.

An Automatic Approach to Reestablish Final Dental Occlusion for 1-Piece Maxillary Orthognathic Surgery.

Accurately establishing a desired final dental occlusion of the upper and lower teeth is a critical step in orthognathic surgical planning. Traditionally, the final occlusion is established by hand-articulating the stone dental models. However, this process is inappropriate to digitally plan the orthognathic surgery using computer-aided surgical simulation. To date, there is no effective method of digitally establishing final occlusion. We propose a 3-stage approach to digitally and automatically establish a desired final dental occlusion for 1-piece maxillary orthognathic surgery, including: 1) to automatically extract points of interest and four key teeth landmarks from the occlusal surfaces; 2) to align the upper and lower teeth to a clinically desired Midline-Canine-Molar relationship by minimization of sum of distances between them; and 3) to finely align the upper and lower teeth to a maximum contact with the constraints of collision and clinical criteria. The proposed method was evaluated qualitatively and quantitatively and proved to be effective and accurate.

Estimating Reference Bony Shape Model for Personalized Surgical Reconstruction of Posttraumatic Facial Defects.

In this paper, we introduce a method for estimating patient-specific reference bony shape models for planning of reconstructive surgery for patients with acquired craniomaxillofacial (CMF) trauma. We propose an automatic bony shape estimation framework using pre-traumatic portrait photographs and post-traumatic head computed tomography (CT) scans. A 3D facial surface is first reconstructed from the patient's pre-traumatic photographs. An initial estimation of the patient's normal bony shape is then obtained with the reconstructed facial surface via sparse representation using a dictionary of paired facial and bony surfaces of normal subjects. We further refine the bony shape model by deforming the initial bony shape model to the post-traumatic 3D CT bony model, regularized by a statistical shape model built from a database of normal subjects. Experimental results show that our method is capable of effectively recovering the patient's normal facial bony shape in regions with defects, allowing CMF surgical planning to be performed precisely for a wider range of defects caused by trauma.

Both the Observer's Expertise and the Subject's Facial Symmetry Can Affect Anatomical Position of the Head.

PURPOSE: It is easier to judge facial deformity when the patient's head is in anatomic position. The purposes of this study were to determine 1) whether a group of expert observers would agree more than a group of nonexperts on what is the correct anatomic position of the head, 2) whether there would be more variation in the alignment of an asymmetrical face compared with a symmetrical one, and 3) whether the alignments of experts would be more repeatable than those of nonexperts. MATERIALS AND METHODS: Thirty-one orthodontists (experts) and 31 dental students (nonexperts) were recruited for this mixed-model study. They were shown randomly oriented 3-dimensional head photographs of an adult with a symmetrical face and an adolescent with an asymmetrical face. In viewing software, the observers oriented the images into anatomic position. They repeated the orientations 4 weeks later. Data were analyzed using a generalized linear model and Bland-Altman plots. The primary predictor variables were experience and symmetry status. The outcome variable was the anatomic position of the head. The other variables of interest included time and orientation direction. RESULTS: There was a statistically significant difference between measurements completed by experts and nonexperts (F1,60 = 14.83; P < .01). The interaction between expertise and symmetrical status showed a statistically significant difference between symmetrical and asymmetrical faces in the expert and nonexpert groups (F1,60 = 9.93; P = .003). The interaction between expertise and time showed a statistically significant difference in measurement over time in the expert and nonexpert groups (F1,60 = 4.66; P = .03). CONCLUSIONS: The study shows that experts can set a head into anatomic position better than nonexperts. In addition, facial asymmetry has a profound effect on the ability of an observer to align a head in the correct anatomic position. Moreover, observer-guided alignment is not reproducible.

An eFTD-VP framework for efficiently generating patient-specific anatomically detailed facial soft tissue FE mesh for craniomaxillofacial surgery simulation.

Accurate surgical planning and prediction of craniomaxillofacial surgery outcome requires simulation of soft tissue changes following osteotomy. This can only be achieved by using an anatomically detailed facial soft tissue model. The current state-of-the-art of model generation is not appropriate to clinical applications due to the time-intensive nature of manual segmentation and volumetric mesh generation. The conventional patient-specific finite element (FE) mesh generation methods are to deform a template FE mesh to match the shape of a patient based on registration. However, these methods commonly produce element distortion. Additionally, the mesh density for patients depends on that of the template model. It could not be adjusted to conduct mesh density sensitivity analysis. In this study, we propose a new framework of patient-specific facial soft tissue FE mesh generation. The goal of the developed method is to efficiently generate a high-quality patient-specific hexahedral FE mesh with adjustable mesh density while preserving the accuracy in anatomical structure correspondence. Our FE mesh is generated by eFace template deformation followed by volumetric parametrization. First, the patient-specific anatomically detailed facial soft tissue model (including skin, mucosa, and muscles) is generated by deforming an eFace template model. The adaptation of the eFace template model is achieved by using a hybrid landmark-based morphing and dense surface fitting approach followed by a thin-plate spline interpolation. Then, high-quality hexahedral mesh is constructed by using volumetric parameterization. The user can control the resolution of hexahedron mesh to best reflect clinicians' need. Our approach was validated using 30 patient models and 4 visible human datasets. The generated patient-specific FE mesh showed high surface matching accuracy, element quality, and internal structure matching accuracy. They can be directly and effectively used for clinical simulation of facial soft tissue change.

Application of A Novel Three-dimensional Printing Genioplasty Template System and Its Clinical Validation: A Control Study.

The purpose of this control study was to assess the accuracy and clinical validation of a novel genioplasty template system. Eighty-eight patients were enrolled and divided into 2 groups: experimental group (using genioplasty templates) and control group (without genioplasty templates). For the experimental group, the templates were designed based on computerized surgical plan and manufactured using three-dimensional printing technique. The template system included a cutting guide and a pair of repositioning guides. For the control group, traditional intraoperative measurements were used without genioplasty templates. The outcome evaluation was completed by comparing planned outcomes with postoperative outcomes. Linear and angular differences for the chin was measured and reported using root mean square deviation (RMSD) and the Bland-Altman method. All surgeries were successfully completed. There was no difficulty to use genioplasty templates. For the experimental group, the largest RMSDs were 1.1 mm in anteroposterior direction and 2.6° in pitch orientation. For the control group without templates, the largest RMSDs were 2.63 mm in superoinferior direction and 7.21° in pitch orientation. Our findings suggest that this genioplasty template system provides greater accuracy in repositioning the chin than traditional intraoperative measurements, and the computerized plan can be transferred accurately to the patient for genioplasty.