Craniofacial identification standards: A review of reliability, reproducibility, and implementation.

There are numerous anatomical and anthropometrical standards that can be utilised for craniofacial analysis and identification. These standards originate from a wide variety of sources, such as orthodontic, maxillofacial, surgical, anatomical, anthropological and forensic literature, and numerous media have been employed to collect data from living and deceased subjects. With the development of clinical imaging and the enhanced technology associated with this field, multiple methods of data collection have become accessible, including Computed Tomography, Cone-Beam Computed Tomography, Magnetic Resonance Imaging, Radiographs, Three-dimensional Scanning, Photogrammetry and Ultrasound, alongside the more traditional in vivo methods, such as palpation and direct measurement, and cadaveric human dissection. Practitioners often struggle to identify the most appropriate standards and research results are frequently inconsistent adding to the confusion. This paper aims to clarify how practitioners can choose optimal standards, which standards are the most reliable and when to apply these standards for craniofacial identification. This paper describes the advantages and disadvantages of each mode of data collection and collates published research to review standards across different populations for each facial feature. This paper does not aim to be a practical instruction paper; since this field encompasses a wide range of 2D and 3D approaches (e.g., clay sculpture, sketch, automated, computer-modelling), the implementation of these standards is left to the individual practitioner.

Multitask Learning Strategy with Pseudo-Labeling: Face Recognition, Facial Landmark Detection, and Head Pose Estimation.

Most facial analysis methods perform well in standardized testing but not in real-world testing. The main reason is that training models cannot easily learn various human features and background noise, especially for facial landmark detection and head pose estimation tasks with limited and noisy training datasets. To alleviate the gap between standardized and real-world testing, we propose a pseudo-labeling technique using a face recognition dataset consisting of various people and background noise. The use of our pseudo-labeled training dataset can help to overcome the lack of diversity among the people in the dataset. Our integrated framework is constructed using complementary multitask learning methods to extract robust features for each task. Furthermore, introducing pseudo-labeling and multitask learning improves the face recognition performance by enabling the learning of pose-invariant features. Our method achieves state-of-the-art (SOTA) or near-SOTA performance on the AFLW2000-3D and BIWI datasets for facial landmark detection and head pose estimation, with competitive face verification performance on the IJB-C test dataset for face recognition. We demonstrate this through a novel testing methodology that categorizes cases as soft, medium, and hard based on the pose values of IJB-C. The proposed method achieves stable performance even when the dataset lacks diverse face identifications.

Relationship between bite force, occlusal contact area, and three-dimensional facial soft tissue in dentofacial deformities.

PURPOSE: This study aimed to investigate three-dimensional facial soft tissue dimensions, maximum bite force (MBF), and occlusal contact area in patients with DFD. In addition, we analyzed the relationship between MBF and the three-dimensional facial measurements. METHODS: Thirty-two patients with skeletal Class III DFD and 20 patients with Class II DFD underwent a soft tissue evaluation using surface laser scanning, as well as MBF and occlusal contact area assessments. The DFD groups were compared with each other and with 25 healthy subjects. RESULTS: Significant morphological differences were found in the transversal, vertical, and anteroposterior dimensions between Class II DFD and Class III DFD. Both DFD groups presented an increased linear distance of chin height, which was strongly related with decreased MBF magnitude. The DFD groups exhibited lower MBF and occlusal contact area, with no significant differences between Class II and Class III DFD. CONCLUSION: The presence of DFD affected 3D measurements of facial soft tissue, causing variations beyond normal limits, lower MBF, and occlusal contact area in both Class II and Class III DFD patients. The vertical dimension might have influenced the lower MBF magnitude in the studied skeletal deformities.

The relevance and accuracy of an AI algorithm-based descriptor on 23 facial attributes in a diverse female US population.

BACKGROUND: The response of AI in situations that mimic real life scenarios is poorly explored in populations of high diversity. OBJECTIVE: To assess the accuracy and validate the relevance of an automated, algorithm-based analysis geared toward facial attributes devoted to the adornment routines of women. METHODS: In a cross-sectional study, two diversified groups presenting similar distributions such as age, ancestry, skin phototype, and geographical location was created from the selfie images of 1041 female in a US population. 521 images were analyzed as part of a new training dataset aimed to improve the original algorithm and 520 were aimed to validate the performance of the AI. From a total 23 facial attributes (16 continuous and 7 categorical), all images were analyzed by 24 make-up experts and by the automated descriptor tool. RESULTS: For all facial attributes, the new and the original automated tool both surpassed the grading of the experts on a diverse population of women. For the 16 continuous attributes, the gradings obtained by the new system strongly correlated with the assessment made by make-up experts (r ≥ 0.80; p < 0.0001) and supported by a low error rate. For the seven categorical attributes, the overall accuracy of the AI-facial descriptor was improved via enrichment of the training dataset. However, some weaker performance in spotting specific facial attributes were noted. CONCLUSION: In conclusion, the AI-automatic facial descriptor tool was deemed accurate for analysis of facial attributes for diverse women although some skin complexion, eye color, and hair features required some further finetuning.

Automatic extraction of facial median sagittal plane for patients with asymmetry based on the EDMA alignment algorithm.

BACKGROUND: We aimed to establish a novel method for automatically constructing three-dimensional (3D) median sagittal plane (MSP) for mandibular deviation patients, which can increase the efficiency of aesthetic evaluating treatment progress. We developed a Euclidean weighted Procrustes analysis (EWPA) algorithm for extracting 3D facial MSP based on the Euclidean distance matrix analysis, automatically assigning weight to facial anatomical landmarks. METHODS: Forty patients with mandibular deviation were recruited, and the Procrustes analysis (PA) algorithm based on the original mirror alignment and EWPA algorithm developed in this study were used to construct the MSP of each facial model of the patient as experimental groups 1 and 2, respectively. The expert-defined regional iterative closest point algorithm was used to construct the MSP as the reference group. The angle errors of the two experimental groups were compared to those of the reference group to evaluate their clinical suitability. RESULTS: The angle errors of the MSP constructed by the two EWPA and PA algorithms for the 40 patients were 1.39 ± 0.85°, 1.39 ± 0.78°, and 1.91 ± 0.80°, respectively. The two EWPA algorithms performed best in patients with moderate facial asymmetry, and in patients with severe facial asymmetry, the angle error was below 2°, which was a significant improvement over the PA algorithm. CONCLUSIONS: The clinical application of the EWPA algorithm based on 3D facial morphological analysis for constructing a 3D facial MSP for patients with mandibular deviated facial asymmetry deformity showed a significant improvement over the conventional PA algorithm and achieved the effect of a dental clinical expert-level diagnostic strategy.

Accuracy and repeatability of fetal facial measurements in 3D ultrasound: A longitudinal study.

OBJECTIVE: Fetal face measurements in prenatal ultrasound can aid in identifying craniofacial abnormalities in the developing fetus. However, the accuracy and reliability of ultrasound measurements can be affected by factors such as fetal position, image quality, and the sonographer's expertise. This study assesses the accuracy and reliability of fetal facial measurements in prenatal ultrasound. Additionally, the temporal evolution of measurements is studied, comparing prenatal and postnatal measurements. METHODS: Three different experts located up to 23 facial landmarks in 49 prenatal 3D ultrasound scans from normal Caucasian fetuses at weeks 20, 26, and 35 of gestation. Intra- and inter-observer variability was obtained. Postnatal facial measurements were also obtained at 15 days and 1 month postpartum. RESULTS: Most facial landmarks exhibited low errors, with overall intra- and inter-observer errors of 1.01 mm and 1.60 mm, respectively. Landmarks on the nose were found to be the most reliable, while the most challenging ones were those located on the ears and eyes. Overall, scans obtained at 26 weeks of gestation presented the best trade-off between observer variability and landmark visibility. The temporal evolution of the measurements revealed that the lower face area had the highest rate of growth throughout the latest stages of pregnancy. CONCLUSIONS: Craniofacial landmarks can be evaluated using 3D fetal ultrasound, especially those located on the nose, mouth, and chin. Despite its limitations, this study provides valuable insights into prenatal and postnatal biometric changes over time, which could aid in developing predictive models for postnatal measurements based on prenatal data.

Enhancement of OCTen faceimages by unsupervised deep learning.

Objective. The quality of optical coherence tomography (OCT)en faceimages is crucial for clinical visualization of early disease. As a three dimensional and coherent imaging, defocus and speckle noise are inevitable, which seriously affect evaluation of microstructure of bio-samples in OCT images. The deep learning has demonstrated great potential in OCT refocusing and denoising, but it is limited by the difficulty of sufficient paired training data. This work aims to develop an unsupervised method to enhance the quality of OCTen faceimages.Approach. We proposed an unsupervised deep learning-based pipeline. The unregistered defocused conventional OCT images and focused speckle-free OCT images were collected by a home-made speckle modulating OCT system to construct the dataset. The image enhancement model was trained with the cycle training strategy. Finally, the speckle noise and defocus were both effectively improved.Main results. The experimental results on complex bio-samples indicated that the proposed method is effective and generalized in enhancing the quality of OCTen faceimages.Significance. The proposed unsupervised deep learning method helps to reduce the complexity of data construction, which is conducive to practical applications in OCT bio-sample imaging.

Body mass index is an overlooked confounding factor in existing clustering studies of 3D facial scans of children with autism spectrum disorder.

Cluster analyzes of facial models of autistic patients aim to clarify whether it is possible to diagnose autism on the basis of facial features and further to stratify the autism spectrum disorder. We performed a cluster analysis of sets of 3D scans of ASD patients (116) and controls (157) using Euclidean and geodesic distances in order to recapitulate the published results on the Czech population. In the presented work, we show that the major factor determining the clustering structure and consequently also the correlation of resulting clusters with autism severity degree is body mass index corrected for age (BMIFA). After removing the BMIFA effect from the data in two independent ways, both the cluster structure and autism severity correlations disappeared. Despite the fact that the influence of body mass index (BMI) on facial dimensions was studied many times, this is the first time to our knowledge when BMI was incorporated into the faces clustering study and it thereby casts doubt on previous results. We also performed correlation analysis which showed that the only correction used in the existing clustering studies-dividing the facial distance by the average value within the face-is not eliminating correlation between facial distances and BMIFA within the facial cohort.

Automated reconstruction: Predictive models based on facial morphology matrices.

Forensic Facial Approximation (FFA) has evolved, with techniques advancing to refine the intercorrelation between the soft-tissue facial profile and the underlying skull. FFA has become essential for identifying unknown persons in South Africa, where the high number of migrant and illegal labourers and many unidentified remains make the identification process challenging. However, existing FFA methods are based on American or European standards, rendering them inapplicable in a South African context. We addressed this issue by conducting a study to create prediction models based on the relationships between facial morphology and known factors, such as population affinity, sex, and age, in white South African and French samples. We retrospectively collected 184 adult cone beam computed tomography (CBCT) scans representing 76 white South Africans (29 males and 47 females) and 108 French nationals (54 males and 54 females) to develop predictive statistical models using a projection onto latent structures regression algorithm (PLSR). On training and untrained datasets, the accuracy of the estimated soft-tissue shape of the ears, eyes, nose, and mouth was measured using metric deviations. The predictive models were optimized by integrating additional variables such as sex and age. Based on trained data, the prediction errors for the ears, eyes, nose, and mouth ranged between 1.6 mm and 4.1 mm for white South Africans; for the French group, they ranged between 1.9 mm and 4.2 mm. Prediction errors on non-trained data ranged between 1.6 mm and 4.3 mm for white South Africans, whereas prediction errors ranging between 1.8 mm and 4.3 mm were observed for the French. Ultimately, our study provided promising predictive models. Although the statistical models can be improved, the inherent variability among individuals restricts the accuracy of FFA. The predictive validity of the models was improved by including sex and age variables and considering population affinity. By integrating these factors, more customized and accurate predictive models can be developed, ultimately strengthening the effectiveness of forensic analysis in the South African region.

Reliability and validity of handheld structured light scanners and a static stereophotogrammetry system in facial three-dimensional surface imaging.

Several new systems for three-dimensional (3D) surface imaging of the face have become available to assess changes following orthognathic or facial surgery. Before they can be implemented in practice, their reliability and validity must be established. Our aim, therefore, was to study the intra- and inter-system reliability and validity of 3dMD (stereophotogrammetry), Artec Eva and Artec Space Spider (both structured light scanners). Intra- and inter-system reliability, expressed in root mean square distance, was determined by scanning a mannequin's head and the faces of healthy volunteers multiple times. Validity was determined by comparing the linear measurements of the scans with the known distances of a 3D printed model. Post-processing errors were also calculated. Intra-system reliability after scanning the mannequin's head was best with the Artec Space Spider (0.04 mm Spider; 0.07 mm 3dMD; 0.08 mm Eva). The least difference in inter-system reliability after scanning the mannequin's head was between the Artec Space Spider and Artec Eva. The best intra-system reliability after scanning human subjects was with the Artec Space Spider (0.15 mm Spider; 0.20 mm Eva; 0.23 mm 3dMD). The least difference in inter-system reliability after scanning human subjects was between the Artec Eva and Artec Space Spider. The most accurate linear measurement validity occurred with the Artec Space Spider. The post-processing error was 0.01 mm for all the systems. The Artec Space Spider is the most reliable and valid scanning system.

A novel approach to the detection of facial wrinkles: Database, detection algorithm, and evaluation metrics.

Skin wrinkles result from intrinsic aging processes and extrinsic influences, including prolonged exposure to ultraviolet radiation and tobacco smoking. Hence, the identification of wrinkles holds significant importance in skin aging and medical aesthetic investigation. Nevertheless, current methods lack the comprehensiveness to identify facial wrinkles, particularly those that may appear insignificant. Furthermore, the current assessment techniques neglect to consider the blurred boundary of wrinkles and cannot differentiate images with varying resolutions. This research introduces a novel wrinkle detection algorithm and a distance-based loss function to identify full-face wrinkles. Furthermore, we develop a wrinkle detection evaluation metric that assesses outcomes based on curve, location, and gradient similarity. We collected and annotated a dataset for wrinkle detection consisting of 1021 images of Chinese faces. The dataset will be made publicly available to further promote wrinkle detection research. The research demonstrates a substantial enhancement in detecting subtle wrinkles through implementing the proposed method. Furthermore, the suggested evaluation procedure effectively considers the indistinct boundaries of wrinkles and is applicable to images with various resolutions.

3D extraoral morphometric changes after implant surgery: an exploratory pilot study using stereophotogrammetry.

OBJECTIVE: This pilot study aimed to evaluate, for the first time, the changes in facial tissues following the placement of a single dental implant. METHOD AND MATERIALS: Patients were scanned with a 3D facial scanner (3dMD) before implant surgery, immediately after surgery (T1), at 7 days postoperatively (T2), and at the impression stage (T3). Acquired images were processed using the 3dMDvultus (3dMD) software program and volume differences and linear depth measurements were calculated to determine the morphometric changes over time. A total of 11 patients were included in the analyses. Descriptive statistics were employed to analyze the data. RESULTS: The volumetric changes and maximum depth differences indicated an initial increase, followed by a progressive decrease in tissue volume after implant placement in the area of the surgery. The volume change values ranged between 2.5 and 3.9 cm3 for T1, whereas for T2, the volume change decreased to a range of 0.8 to 1.8 cm3. Maximum depth differences ranged between 2.06 and 2.80 mm in the soft tissues immediately after the implant surgery and reduced to around 2.01 to 0.55 mm at the impression stage. The amount of painkiller used was not related to the magnitude of linear depth measurements at any assessed time point. CONCLUSION: There was a longitudinal decrease in soft tissue volume and depth difference in extraoral soft tissues in the region of implant placement after surgery up to 6 weeks. The use of a facial scanner is a promising noninvasive method to monitor 3D morphometric changes after implant surgery.

Facial soft-tissue thickness in children: A study of a CTBC Brazilian sample.

As an auxiliary method in the process of human identification, forensic facial approximation (FFA) is an important tool for identifying unknown human bodies whose remains do not present the necessary traceability to any antemortem data collection. Specific characteristics are necessary when addressing children aged between 6 and 10 years, who have little sexual differentiation and a mixed dentition. Due to the chronology of eruption of the permanent second molars in this population, it is not possible to measure facial soft-tissue thickness (FSTT) from specific landmarks such as supra and infra M2. The objective of this research was to report the method for measuring the average FSTT of 32 landmarks adapting the method for adults replacing the landmarks at the upper and lower second molars (Supra M2 and Infra M2) in children up to 10 years of age for a measurement using the deciduous second molars as reference. We found statistical differences for some points, considering the variables of age and sex, but with a maximum difference of 2 mm, which allows the use of a single FSTT table. The deciduous teeth can replace the reference of the thicknesses at the supra and infra M2 landmarks. In addition to the new FSTT data for children in Brazil, we concluded that the proposed adaptation to the deciduous M2 points can be applied to obtain soft-tissue data for 32 facial points.

Establishment and verification of a method for analyzing nasal blackheads images.

BACKGROUND: As people pay more attention to their skin health and the demand of developing skin care products for facial blackheads grows, the value of objective and efficient image recognition methods for blackheads is becoming more evident. Inspired by this current situation, this study attempted to analyze the number of blackheads of different severity automatically on the nose using an object recognition method on photographs of the nasal blackheads of subjects. METHOD: This study collected 350 subjects' facial photos in the laboratory environment, who aged 18-60, with blackhead symptoms in the nasal region. And expert assessment was used as a reference for machine learning to verify the performance of the nasal blackhead image recognition model through consistency and correlation analysis. RESULTS: The study concluded that the algorithm accuracy reached above 0.9, the model itself was effective, and the consistency between the model and the expert assessor assessment results was good, with the number of nasal blackheads, the count of blackheads of different severity, and the intra-group correlation coefficient ICC of blackhead severity all above 0.9, indicating that the deep learning-based assessment model had high overall performance and the evaluation results were comparable to those of the expert assessor. CONCLUSION: The recognition and analyzing model of nasal blackhead images provides a scientifically objective and accurate method for identifying the number and evaluating the severity of nasal blackheads. By using this model, the efficiency of evaluating nasal blackhead images in the cosmetics clinical trial will be improved. The assessment result of nasal blackheads will be objective and stable, and not only rely on the professional knowledge and clinical experience of assessors. The model can try to be applied in cosmetics efficacy testing and continuously optimized.

High-frequency Ultrasound in the Assessment before and after Applying HArmonyCa™.

AIM: To describe a clinical case of ultrasound (US) used to evaluate, before, post-immediately, and after 4 months, the facial application of a volumizing and biostimulating substance. BACKGROUND: Detecting the behavior of injected filler materials with high-frequency US-guided application is the future of natural facial rejuvenation with more predictable and satisfactory results. TECHNIQUE: A patient indicated for orofacial harmonization (OFH) procedures through volumizing and biostimulating material application was invited to participate. The technique was performed by applying HArmonyCa™ (Allergan Aesthetics, Irvine, CA, USA) in the gonial, preauricular, and bilateral lateral zygomatic angle regions. The first evaluations used the US images before and after product application with a Logiq e® high-frequency US device (GE Healthcare, Chicago, IL, USA) with a probe/linear transducer of 18 MHz. About 4 months after the procedure, a new assessment with the same initial acquisition pattern was performed. The first evaluation showed normal-looking anatomical structures without the esthetic material. Immediately after the procedure and 4 months later, the assessments presented semi-permanent esthetic fillers as dispersed lobulated hyperechogenic areas with a cloud aspect. CONCLUSION: High-frequency US was efficient in the static evaluation of HArmonyCa™ behavior on the facial skin. CLINICAL SIGNIFICANCE: The US-guided application of injectable products in specific areas has minimal side effects and contributes to more predictable and satisfactory results. How to cite this article: Gouveia RSA, Tostes LLL, Bezerra FV, et al. High-frequency Ultrasound in the Assessment before and after Applying HArmonyCa™. J Contemp Dent Pract 2024;25(1):10-14.

Fully automated landmarking and facial segmentation on 3D photographs.

Three-dimensional facial stereophotogrammetry provides a detailed representation of craniofacial soft tissue without the use of ionizing radiation. While manual annotation of landmarks serves as the current gold standard for cephalometric analysis, it is a time-consuming process and is prone to human error. The aim in this study was to develop and evaluate an automated cephalometric annotation method using a deep learning-based approach. Ten landmarks were manually annotated on 2897 3D facial photographs. The automated landmarking workflow involved two successive DiffusionNet models. The dataset was randomly divided into a training and test dataset. The precision of the workflow was evaluated by calculating the Euclidean distances between the automated and manual landmarks and compared to the intra-observer and inter-observer variability of manual annotation and a semi-automated landmarking method. The workflow was successful in 98.6% of all test cases. The deep learning-based landmarking method achieved precise and consistent landmark annotation. The mean precision of 1.69 ± 1.15 mm was comparable to the inter-observer variability (1.31 ± 0.91 mm) of manual annotation. Automated landmark annotation on 3D photographs was achieved with the DiffusionNet-based approach. The proposed method allows quantitative analysis of large datasets and may be used in diagnosis, follow-up, and virtual surgical planning.

Three-dimensional facial soft-tissue changes after surgical orthodontics in different vertical facial types of skeletal Class III malocclusion: A retrospective study.

The study compared the soft-tissue response to hard-tissue movement among different Class III vertical facial types after orthognathic surgery (OGS). The study included 90 consecutive adult patients with skeletal Class III malocclusion who underwent two-jaw OGS. Patients were divided into three groups (high, medium, and low angle) based on the presurgical Frankfort-mandibular plane angle. Cone-beam computerized tomographs were taken before surgery and after debonding. Soft- and hard-tissue linear and angular measurements were performed using three-dimensional reconstruction images. One-way analysis of variance was used for intergroup comparisons. Soft tissue tended to respond more to hard-tissue movement in the lower lip area in patients with low angle (mean = 0.089, SD = 0.047, p = 0.023), whereas no significant difference was observed for other sites. Consistently, L1/Li thickness increased most significantly in the high-angle group (mean = 1.98, SD = 2.14, p = 0.0001), and B/Si thickness decreased most significantly after surgery (mean = 2.16, SD = 2.68, p = 0.016). The findings suggest that the high-angle group had a higher chance of undergoing genioplasty to enhance chin contour. Different OGS plans should be considered for different Class III vertical facial types.

Correlation analysis of airway-facial phenotype in Crouzon syndrome by geometric morphometrics: A promising method for non-radiation airway evaluation.

AIM: This study aimed to verify the correlation of the airway-facial phenotype and visualize the morphological variation in Crouzon syndrome patients. Additionally, to develop a non-radiation methodology for airway assessments. METHOD: In this study, 22 patients diagnosed with Crouzon syndrome (Age: 7.80 ± 5.63 years; Gender distribution: 11 females and 11 males) were analysed. The soft tissue surface and airway were three-dimensionally reconstructed, and the entire facial phenotype was topologized and converted into spatial coordinates. Geometric morphometrics was employed to verify the correlation and visualize dynamic phenotypic variation associated with airway volume. A total of 276 linear variables were automatically derived from 24 anatomical landmarks, and principal component analysis (PCA) identified the 20 most significant parameters for airway evaluation. Correlation analyses between parameters and airway volume were performed. Then, patients were classified into three groups based on airway volume, and the differences among the groups were compared for evaluating the differentiating effectiveness of parameters. RESULTS: The facial phenotype was strongly correlated with the airway (coefficient: 0.758). Morphological variation was characterized by (i) mandibular protrusion and anticlockwise rotation; (ii) midface retrusion; (iii) supraorbital frontward and (iv) lengthening of the facial height. All the anthropometric parameters were strongly associated with the airway, and the differences among the groups were statistically significant. CONCLUSION: This study confirmed the strong correlation between facial phenotype and airway parameters in Crouzon syndrome patients. Despite the development of the airway, pathological midface retrusion was still aggravated, suggesting that surgical intervention was inevitable. Three-dimensional facial anthropometry has potential as a non-radiation examination for airway evaluation.

[Preliminary evaluation of chin symmetry with three dimentional soft tissue spatial angle wireframe template].

OBJECTIVE: To develop an efficient and robust method based on three dimensional facial landmarks for evaluating chin region asymmetry at the soft tissue level and to compare it with the traditional mirror-overlap analysis method in order to test its availability. METHODS: Standard symmetrical face was used for mental tubercle coordinate transformation so as to filter soft tissue three dimensional spatial angle and construct corresponding three dimensional spatial angle wireframe template. Ten patients aged 12-32 years with clinical chin region asymmetry diagnosis at the Department of Orthodontics of Peking University Hospital of Stomatology from November 2020 to November 2021 were randomly selected. Three dimensional soft tissue face scan data of the patients were collected by three dimensional face scanner and the landmark points were automatically determined by the Meshmonk non-rigid registration algorithm program, and in this way, the asymmetric three dimensional spatial angle wireframe template and corresponding spatial angle parameters were generated. Mirror-overlap analysis of face scan data was also performed in Geomagic Studio 2015 software and deviation color maps were generated. This study took mirror-overlap analysis as the gold standard method, the response rate of chin region asymmetry was eva-luated by the outcomes of the mirror-overlap analysis and three dimensional spatial angle wireframe template analysis. RESULTS: Nine three dimensional spatial angle indicators were selected through coordinate transformation, and the response rate was calculated using mirror-overlap analysis as the gold standard method. Among these ten selected patients, the response rate of the total chin region asymmetry was 90% (9/10). Using the deviation value of mirror-overlap analysis as a reference, the response rate of chin region asymmetry in the X dimension was 86%, the response rate of chin region asymmetry in the Y dimension was 89%, and the response rate of chin region asymmetry in the Z dimension was 100%. CONCLUSION: The three dimensional soft tissue spatial angle wireframe template proposed in this study has some feasibility in evaluating chin region asymmetry at the soft tissue level, and its ability to recognize asymmetry separately in the three dimensional direction is better than the mirror-overlap analysis method, and the indicators recognition rate still needs to be further improved.