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.

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.

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.

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.

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.

Skin patch based makeup finish assessment technique by deep neural network.

BACKGROUND: Skin color and texture play a significant role in influencing impressions. To understand the influence of skin appearance and to develop better makeup products, objective evaluation methods for makeup finish have been explored. This study aims to apply machine learning technology, specifically deep neural network (DNN), to accurately analyze and evaluate delicate and complex cosmetic skin textures. METHODS: "Skin patch datasets" were extracted from facial images and used to train a DNN model. The advantages of using skin patches include retaining fine texture, eliminating false correlations from non-skin features, and enabling visualization of the inferred results for the entire face. The DNN was trained in two ways: a classification task to classify skin attributes and a regression task to predict the visual assessment of experts. The trained DNNs were applied for the evaluation of actual makeup conditions. RESULTS: In the classification task training, skin patch-based classifiers for age range, presence or absence of base makeup, formulation type (powder/liquid) of the applied base makeup, and immediate/while after makeup application were developed. The trained DNNs on regression task showed high prediction accuracy for the experts' visual assessment. Application of DNN to the evaluation of actual makeup conditions clearly showed appropriate evaluation results in line with the appearance of the makeup finish. CONCLUSION: The proposed method of using DNNs trained on skin patches effectively evaluates makeup finish. This approach has potential applications in visual science research and cosmetics development. Further studies can explore the analysis of different skin conditions and the development of personalized cosmetics.

Enhancing Face Recognition With Detachable Self-Supervised Bypass Networks.

Attributed to the development of deep networks and abundant data, automatic face recognition (FR) has quickly reached human-level capacity in the past few years. However, the FR problem is not perfectly solved in case of large poses and uncontrolled occlusions. In this paper, we propose a novel bypass enhanced representation learning (BERL) method to improve face recognition under unconstrained scenarios. The proposed method integrates self-supervised learning and supervised learning together by attaching two auxiliary bypasses, a 3D reconstruction bypass and a blind inpainting bypass, to assist robust feature learning for face recognition. Among them, the 3D reconstruction bypass enforces the face recognition network to encode pose independent 3D facial information, which enhances the robustness to various poses. The blind inpainting bypass enforces the face recognition network to capture more facial context information for face inpainting, which enhances the robustness to occlusions. The whole framework is trained in end-to-end manner with two self-supervised tasks above and the classic supervised face identification task. During inference, the two auxiliary bypasses can be detached from the face recognition network, avoiding any additional computational overhead. Extensive experimental results on various face recognition benchmarks show that, without any cost of extra annotations and computations, our method outperforms state-of-the-art methods. Moreover, the learnt representations can also well generalize to other face-related downstream tasks such as the facial attribute recognition with limited labeled data.

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.

[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.

The Doppelgänger effect? A comparative study of forensic facial depiction methods.

This study attempted to assess the reproducibility of 2D and 3D forensic methods for facial depiction from skeletal remains (2D sketch, 3D manual, 3D automated, 3D computer-assisted). In a blind study, thirteen practitioners produced fourteen facial depictions, using the same skull model derived from CT data of a living donor, a biological profile and relevant soft tissue data. The facial depictions were compared to the donor subject using three different evaluation methods: 3D geometric, 2D face recognition ranking and familiar resemblance ratings. Five of the 3D facial depictions (all 3D methods) demonstrated a deviation error within ± 2 mm for ≥ 50% of the total face surface. Overall, no single 3D method (manual, computer assisted, automated) produced consistently high results across all three evaluations. 2D comparisons with a facial photograph of the donor were carried out for all the 2D and 3D facial depictions using four freely available face recognition algorithms (Toolpie; Photomyne; Face ++; Amazon). The 2D sketch method produced the highest ranked matches to the donor photograph, with overall ranking in the top six. Only one 3D facial depiction was ranked highly in both the 3D geometric and 2D face recognition comparisons. The majority (67%) of the facial depictions were rated as limited or moderate resemblance by the familiar examiner. Only one 2D facial depiction was rated as strong resemblance, whilst two 2D sketches and two 3D facial depictions were rated as good resemblances by the familiar examiner. The four most geometrically accurate 3D facial depictions were only rated as limited or moderate resemblance to the donor by the familiar examiner. The results suggest that where a consistent facial depiction method is utilised, we can expect relatively consistent metric reliability between practitioners. However, presentation standards for practitioners would greatly enhance the possibility of recognition in forensic scenarios.

Evaluation of the anterior dentoalveolar relationship in skeletal Class III malocclusion patients with different vertical facial patterns using cone-beam computed tomography.

OBJECTIVES: To measure and compare labiolingual inclinations of the teeth and alveolar bone and the anterior dentoalveolar inclination in patients with skeletal Class III malocclusions with different vertical facial patterns using cone-beam computed tomography (CBCT). MATERIALS AND METHODS: Based on the inclusion and exclusion criteria, 84 CBCT images of patients with untreated skeletal Class III malocclusion were selected. There were 28 patients each in the hypo-, normo-, and hyperdivergent groups. The labiolingual inclinations of the teeth, the corresponding alveolar bone, and the anterior dentoalveolar inclinations were measured and analyzed statistically. RESULTS: The inclinations of the mandibular canine and corresponding alveolar bone were smaller in the hypodivergent group than in the hyperdivergent group. The inclination of the alveolar bone and the maxillary dentoalveolar inclination were smaller in the hyperdivergent group than in the hypodivergent group. CONCLUSIONS: There were differences in the inclination of the teeth, corresponding alveolar bone, and dentoalveolar inclinations at different positions among skeletal Class III patients with different vertical facial patterns. The roots were generally located on the labial side of the alveolar bone.

Differences in the annotation between facial images and videos for training an artificial intelligence for skin type determination.

BACKGROUND: The Grand-AID research project, consisting of GRANDEL-The Beautyness Company, the dermatology department of Augsburg University Hospital and the Chair of IT Infrastructure for Translational Medical Research at Augsburg University, is currently researching the development of a digital skin consultation tool that uses artificial intelligence (AI) to analyze the user's skin and ultimately perform a personalized skin analysis and a customized skin care routine. Training the AI requires annotation of various skin features on facial images. The central question is whether videos are better suited than static images for assessing dynamic parameters such as wrinkles and elasticity. For this purpose, a pilot study was carried out in which the annotations on images and videos were compared. MATERIALS AND METHODS: Standardized image sequences as well as a video with facial expressions were taken from 25 healthy volunteers. Four raters with dermatological expertise annotated eight features (wrinkles, redness, shine, pores, pigmentation spots, dark circles, skin sagging, and blemished skin) with a semi-quantitative and a linear scale in a cross-over design to evaluate differences between the image modalities and between the raters. RESULTS: In the videos, most parameters tended to be assessed with higher scores than in the images, and in some cases significantly. Furthermore, there were significant differences between the raters. CONCLUSION: The present study shows significant differences between the two evaluation methods using image or video analysis. In addition, the evaluation of the skin analysis depends on subjective criteria. Therefore, when training the AI, we recommend regular training of the annotating individuals and cross-validation of the annotation.

Automatic labeling of facial zones for digital clinical application: An ensemble of semantic segmentation models.

INTRODUCTION: The application of artificial intelligence to facial aesthetics has been limited by the inability to discern facial zones of interest, as defined by complex facial musculature and underlying structures. Although semantic segmentation models (SSMs) could potentially overcome this limitation, existing facial SSMs distinguish only three to nine facial zones of interest. METHODS: We developed a new supervised SSM, trained on 669 high-resolution clinical-grade facial images; a subset of these images was used in an iterative process between facial aesthetics experts and manual annotators that defined and labeled 33 facial zones of interest. RESULTS: Because some zones overlap, some pixels are included in multiple zones, violating the one-to-one relationship between a given pixel and a specific class (zone) required for SSMs. The full facial zone model was therefore used to create three sub-models, each with completely non-overlapping zones, generating three outputs for each input image that can be treated as standalone models. For each facial zone, the output demonstrating the best Intersection Over Union (IOU) value was selected as the winning prediction. CONCLUSIONS: The new SSM demonstrates mean IOU values superior to manual annotation and landmark analyses, and it is more robust than landmark methods in handling variances in facial shape and structure.

Disastrous cerebral and ocular vascular complications after cosmetic facial filler injections: a retrospective case series study.

Soft tissue filler injections are among the most popular facial rejuvenation methods. Cerebral infarction and ophthalmic artery occlusion are rare and catastrophic complications, especially when facial cosmetic fillers are injected by inexperienced doctors. Radiologists and plastic surgeons need to increase their awareness of the complications associated with fillers, which allows early diagnosis and intervention to improve patient prognosis. Regarding the mechanism by which vascular occlusion occurs after facial filler injections, a retrograde embolic mechanism is currently the predominant theory. Numerous case reports have been presented regarding complications associated with injections of facial aesthetics. However, the small sample sizes of these studies did not allow for an adequate assessment of the clinical and imaging manifestations based on the location of the occlusion and the type of filler, and detailed elaboration of multiple cerebral infarctions is also lacking. Therefore, this study aimed to investigate the clinical and radiological features of severe cerebral and ocular complications caused by cosmetic facial filler injections. In addition, we discuss the pathogenesis, treatment, and prognosis of these patients. The clinical, computed tomography (CT), magnetic resonance imaging (MRI), and digital subtraction angiography (DSA) findings were described and analysed. Radiological examinations are crucial for demonstrating severe complications, and brain MRI is especially strongly suggested for patients with cosmetic filler-induced vision loss to identify asymptomatic cerebral infarctions. Extreme caution and care should be taken during facial injections by plastic surgeons.

Comparison of the effects on facial soft tissues produced by rapid and slow maxillary expansion using stereophotogrammetry: a randomized clinical trial.

OBJECTIVE: To compare the effects on facial soft tissues produced by maxillary expansion generated by rapid maxillary expansion (RME) versus slow maxillary expansion (SME). MATERIALS AND METHODS: Patients in the mixed dentition were included with a transverse discrepancy between the two arches of at least 3 mm. A conventional RME screw was compared to a new expansion screw (Leaf expander) designed to produce SME. Both screws were incorporated in a fixed expander. The primary outcome was the difference of the facial tissue changes in the nasal area measured on facial 3D images captured immediately before application of the expander (T0) and after one year of retention, immediately after the expander removal (T1). Secondary outcomes were soft tissue changes of other facial regions (mouth, lips, and chin). Analysis of covariance was used for statistical analysis. RESULTS: Fourteen patients were allocated to the RME group, and 14 patients were allocated to the SME group. There were no dropouts. Nasal width change showed a difference between the two groups (1.3 mm greater in the RME group, 95% CI from 0.4 to 2.2, P = 0.005). Also, intercanthal width showed a difference between treatments (0.7 mm greater in the RME group, 95% CI from 0.0 to 1.3, P = 0.044). Nasal columella width, mouth width, nasal tip angle, upper lip angle, and lower lip angle did not show any statistically significant differences. The Y-axis (anterior-posterior) components of the nasal landmark showed a statistically significant difference between the two groups (0.5 mm of forward displacement greater in the RME group, 95% CI from 0.0 to 1.2, P = 0.040). Also, Z-axis (superior-inferior) components of the lower lip landmark was statistically significant (0.9 mm of downward displacement in favor of the RME group, 95% CI from 0.1 to 1.7, P = 0.027). All the other comparisons of the three-dimensional assessments were not statistically significant. CONCLUSIONS: RME produced significant facial soft tissue changes when compared to SME. RME induced greater increases in both nasal and intercanthal widths (1.3 mm and 0.7 mm, respectively). These findings, though statistically significant, probably are not clinically relevant. Trial registration ISRCTN, ISRCTN18263886. Registered 8 November 2016, https://www.isrctn.com/ISRCTN18263886?q=Franchi&filters=&sort=&offset=2&totalResults=2&page=1&pageSize=10.

Three-dimensional analysis of hard and soft tissue changes in skeletal class II patients with high mandibular plane angle undergoing surgery.

This study aimed to study 3-dimensional (3D) changes of hard and soft tissues of skeletal class II patients after 2-jaw surgery and genioplasty. 32 adult patients diagnosed with mandibular hypoplasia who underwent 2-jaw surgery of maxillary impaction, mandibular advancement and genioplasty were enrolled. Cone-beam computed tomography and 3D stereophotogrammetry was conducted 1 week before and 6 months after surgery. Dolphin imaging software was used to establish a 3D digitizing model and 3D measurement system. Paired t-test was performed to compare the values before and after surgery. Pearson's correlation test assessed the degree of correlations between hard and soft tissue change. The mean impaction of the maxilla was 2.600 ± 3.088 mm at A. The mean advancement of the mandible was 7.806 ± 2.647 mm at B. There was a significant upward and forward movement for most landmarks of the nose and lip, while a significant decrease in nasal tip height (lateral view), upper lip height, and upper and lower vermilion height. The nose's width was significantly increased. For maxillary, Sn, Ac-r, Ac-l, and Ls demonstrated a significant correlation with A and U1 in the anteroposterior axis. However, there were no significant correlations among them in the vertical axis. For mandibular, Li demonstrated a significant correlation with L1 in the anteroposterior axis specifically for the mandible. Notably, correlations between the landmarks of the chin's hard and soft tissues were observed across all axes. The utilization of 3-D analysis facilitated a quantitative comprehension of both hard and soft tissues, thereby furnishing valuable insights for the strategic formulation of orthognathic treatment plans targeting patients with skeletal class II conditions.

Image-based investigation of lip aging features in a large number of Korean women.

BACKGROUND: The lips play a significant role in shaping facial aesthetics. Due to the distinct attributes of lips in contrast to other facial skin, a unique approach is imperative for managing lip aging. We analyzed lip characteristics (morphology, wrinkles, and color) to investigate visual changes and distinctive attributes of aging lips. METHODS: By utilizing image data processing methods, including facial landmark detection, pattern recognition, and color quantification, we extracted 11 lip characteristic indices (four morphological indices, four wrinkle indices, and three color indices) from high-resolution images of 1000 Korean women aged 20-69. Correlation tests were conducted to assess the relationship between lip characteristic indices and age, and also between lip morphological and wrinkle indices. RESULTS: Lip height significantly decreased, while lip width and lip ratio (lip width divided by the sum of the upper and lower lip height) significantly increased with aging. Lip wrinkles significantly increased with aging, whereas lip colors (redness and yellowness) decreased. The lip wrinkle indices, which are segmented for the first time in this study, exhibited significant correlations with lip width, and three of them additionally were correlated with lip ratio (p < 0.05). The results imply such morphological changes can be associated with wrinkle formation of human lips. CONCLUSION: The indices suggested in this study can be used for assessing lip aging characteristics, and the study results can contribute to deeper understanding of lip aging.

Superimposition Study to Determine the Angular Arterial Distribution and Its Clinical Application.

BACKGROUND: The purpose of this study was to determine the distribution of the angular artery (AA) in the medial canthal area with the aim of defining an arterial course to prevent AA injury during facial surgery in this region. METHODS: The authors dissected 36 hemifaces of 18 cadavers. The horizontal distance from the vertical level through the medial canthus to the AAs was measured. The AA course of each specimen was then recorded, and all of them were then superimposed to determine the AA course. The diameter and depth of the AA around the medial canthal area were also investigated using ultrasonography on living subjects. RESULTS: The horizontal distances from the medial canthus level and 2 cm below the medial canthus were 9.0 ± 2.0 mm (mean ± SD) and 1.9 ± 2.4 mm, respectively. The superimposed image demonstrated that most of the AAs were present inside the vertical line through the medial canthus. Ultrasonography indicated that the AA was 2.3 ± 0.9 mm below the skin and 1.7 ± 0.3 mm in diameter. CONCLUSIONS: The AA course was relatively constant along the nasojugal fold. The AAs were most often present between the middle of the medial canthus and the facial midline, but were very scarce in both the medial and lateral thirds. Knowledge of the detailed course of the AA may help surgeons to avoid arterial injury and decrease the risk of surgical morbidities around the nasal root and medial canthal area.

Analysis of Age-Related Changes in Lower Facial Fat Compartments and of the Course of Blood Vessels Using Computed Tomography.

BACKGROUND: According to the volume restoration theory, lower facial fat compartments tend to selectively atrophy or hypertrophy with age. The aim of this study was to demonstrate age-related changes in lower facial fat compartments using computed tomography, with strict control of the body mass index and underlying diseases. METHODS: This study included 60 adult women in three age-based categories. The thicknesses of the jowl, labiomandibular, and chin fat compartments were measured using computed tomographic images. The distribution and arrangement of facial blood vessels were further analyzed to provide evidence of the safety of rejuvenation strategies based on the facial volumetric theory. RESULTS: The inferior part of the superficial jowl fat compartment and deep jowl fat compartment thickened with age. The deep layer of the labiomandibular fat compartment thinned with age, and the superficial layer thickened with age. The deep and superficial layers of the chin compartments thickened with age. The facial vein passes through the lower mandibular border at the anterior edge of the masseter muscle and moves upward, perpendicular to the lower mandibular border. The high-risk area of the facial artery had an angle of approximately 45 degrees to the lower mandibular border. CONCLUSIONS: This study suggests that with age, selective thickening or thinning occurs in different lower facial fat compartments. The mandible and masseter muscle were used as reference markers to analyze the courses of the facial artery and facial vein, which can help clinicians to reduce vascular injury.

Comparison of individualized facial growth prediction models based on the partial least squares and artificial intelligence.

OBJECTIVES: To compare facial growth prediction models based on the partial least squares and artificial intelligence (AI). MATERIALS AND METHODS: Serial longitudinal lateral cephalograms from 410 patients who had not undergone orthodontic treatment but had taken serial cephalograms were collected from January 2002 to December 2022. On every image, 46 skeletal and 32 soft-tissue landmarks were identified manually. Growth prediction models were constructed using multivariate partial least squares regression (PLS) and a deep learning method based on the TabNet deep neural network incorporating 161 predictor, and 156 response, variables. The prediction accuracy between the two methods was compared. RESULTS: On average, AI showed less prediction error by 2.11 mm than PLS. Among the 78 landmarks, AI was more accurate in 63 landmarks, whereas PLS was more accurate in nine landmarks, including cranial base landmarks. The remaining six landmarks showed no statistical difference between the two methods. Overall, soft-tissue landmarks, landmarks in the mandible, and growth in the vertical direction showed greater prediction errors than hard-tissue landmarks, landmarks in the maxilla, and growth changes in the horizontal direction, respectively. CONCLUSIONS: PLS and AI methods seemed to be valuable tools for predicting growth. PLS accurately predicted landmarks with low variability in the cranial base. In general, however, AI outperformed, particularly for those landmarks in the maxilla and mandible. Applying AI for growth prediction might be more advantageous when uncertainty is considerable.