Mild cognitive impairment prediction based on multi-stream convolutional neural networks.

BACKGROUND: Mild cognitive impairment (MCI) is the transition stage between the cognitive decline expected in normal aging and more severe cognitive decline such as dementia. The early diagnosis of MCI plays an important role in human healthcare. Current methods of MCI detection include cognitive tests to screen for executive function impairments, possibly followed by neuroimaging tests. However, these methods are expensive and time-consuming. Several studies have demonstrated that MCI and dementia can be detected by machine learning technologies from different modality data. This study proposes a multi-stream convolutional neural network (MCNN) model to predict MCI from face videos. RESULTS: The total effective data are 48 facial videos from 45 participants, including 35 videos from normal cognitive participants and 13 videos from MCI participants. The videos are divided into several segments. Then, the MCNN captures the latent facial spatial features and facial dynamic features of each segment and classifies the segment as MCI or normal. Finally, the aggregation stage produces the final detection results of the input video. We evaluate 27 MCNN model combinations including three ResNet architectures, three optimizers, and three activation functions. The experimental results showed that the ResNet-50 backbone with Swish activation function and Ranger optimizer produces the best results with an F1-score of 89% at the segment level. However, the ResNet-18 backbone with Swish and Ranger achieves the F1-score of 100% at the participant level. CONCLUSIONS: This study presents an efficient new method for predicting MCI from facial videos. Studies have shown that MCI can be detected from facial videos, and facial data can be used as a biomarker for MCI. This approach is very promising for developing accurate models for screening MCI through facial data. It demonstrates that automated, non-invasive, and inexpensive MCI screening methods are feasible and do not require highly subjective paper-and-pencil questionnaires. Evaluation of 27 model combinations also found that ResNet-50 with Swish is more stable for different optimizers. Such results provide directions for hyperparameter tuning to further improve MCI predictions.

Prioritising quality: investigating the influence of image quality on forensic facial comparison.

Morphological analysis in forensic facial comparison was recently validated for judicial use. However, no image quality assurance systems exist for this purpose, despite image triage being considered the best practice approach. Hence, this study aimed at testing a semi-quantitative scoring method to assess image quality and investigated facial image resolution and lighting quality quantitatively in a context of forensic facial comparison. For this purpose, 400 facial comparison photographic and CCTV image pools developed from the Wits Face Database were used. These facial images were analysed in prior studies that investigated the validity of morphological analysis. A semi-quantitative image quality scoring system was adapted and tested on the above sample and compared across correct and incorrect matches obtained as part of previous studies using a logistic regression model. In addition, facial images were cropped to the closest pixel comprising the face, head and neck areas; then, a face-to-image pixel proportion was calculated as an estimator of resolution quality; and pixel exposure qualities were obtained to be compared to facial comparison outcomes. Ideal and high image quality scores were related to correctness of matches, while low-quality scores were related to incorrect matches. High pixel proportions were related to true matches and low exposure was related to false positives, while high exposure was related to false negatives. These results suggest that an easy method for image triage could be employed by scoring image quality. Quantitative measures should be investigated further for thresholding quality suitability for confidence of facial comparisons.

Facial cutaneous tuberculosis infected by non-tuberculous mycobacteria.

BACKGROUND: Cutaneous infections caused by non-tuberculous mycobacteria (NTM) are extremely rare, particularly when they are localized to the facial area. This condition presents significant diagnostic challenges due to its unusual presentation and the need for precise microbiological identification. CASE PRESENTATION: A two-year-old male patient presented with a progressively enlarging reddish-brown mass on the left side of his face. Despite the absence of systemic symptoms, the lesion's growth warranted investigation due to its growth. Ultrasonography showed a hypoechoic mass in the dermis, indicating an underlying abscess. The subsequent aspiration resulted in pale yellow pus, which upon testing and culture, confirmed the presence of Mycobacterium avium complex infection, a species of NTM. This case exemplifies the synergy between imaging modalities and microbiological analysis, highlighting the crucial role of both in achieving favorable clinical outcomes in patients with suspected cutaneous NTM infections. Ultrasound can expedite diagnosis, improve treatment planning, and enhance patient care by enabling targeted interventions and monitoring response to therapy in these scenarios. However, it is the combination of pathogen-specific diagnostics that ensures accurate etiological attribution and appropriate antimicrobial stewardship. CONCLUSION: Although rare, facial cutaneous infections caused by NTM still deserve thorough investigation to determine the exact cause. Ultrasound is used to identify cutaneous lesions, measure their extent, and guide surgical procedures. The ultimate diagnosis is based on microbiological confirmation.

Prenatal diagnosis of a severe form of frontonasal dysplasia with severe limb anomalies, hydrocephaly, a hypoplastic corpus callosum, and a ventricular septal defect using 3D ultrasound: a case report and literature review.

BACKGROUND: Frontonasal dysplasia (FND) is a rare congenital anomaly resulting from the underdevelopment of the frontonasal process, and it can be syndromic or nonsyndromic. The typical features of FND include a deformed nose and ocular hypertelorism, which are sometimes associated with cleft lip and/or palate. Only approximately 10 cases of prenatally diagnosed nonsyndromic FND have been reported in the past 30 years. CASE PRESENTATION: A 33-year-old woman (G2P1) was referred to our center at 20 gestational weeks for bilateral hydrocephaly. We detected typical features of FND, including severe hypertelorism, median nasal bifidity, a minor cleft lip, and multiple limb anomalies using three-dimensional (3D) ultrasound. A hypoplastic corpus callosum, unilateral microtia, and a ventricular septal defect were also detected. Genetic testing, including karyotype analysis, copy number variation (CNV) analysis, trio-whole exome sequencing (trio-WES), and trio-whole-gene sequencing (trio-WGS), was performed; however, we did not find any de novo gene variants in the fetus as compared to the parents. Postmortem examination confirmed the prenatal diagnosis of FND. CONCLUSION: The present case expands the wide phenotypic spectrum of prenatal FND patients. 3D ultrasound is a useful tool for detecting facial and limb deformities.

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.

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.

A smartphone application for personalized facial aesthetic monitoring.

BACKGROUND: Methods available at home for capturing facial images to track changes in skin quality and evaluate skincare treatments are limited. In this study, we developed a smartphone camera application (app) for personalized facial aesthetic monitoring. MATERIALS AND METHODS: A face alignment indicators (FAIN) system utilizing facial landmark detection, an artificial intelligence technique, to estimate key facial parts, was implemented into the app to maintain a consistent facial appearance during image capture. The FAIN system is composed of a fixed target indicator and an alignment indicator that dynamically changes its shape according to the user's face position, size, and orientation. Users align their faces to match the alignment indicator with the fixed target indicator, and the image is automatically captured when alignment is achieved. RESULTS: We investigated the app's effectiveness in ensuring a consistent facial appearance by analyzing both geometric and colorimetric data. Geometric information from captured faces and colorimetric data from stickers applied to the faces were utilized. The coefficients of variation (CVs) for the L*, a*, and b* values of the stickers were higher compared to those measured by a colorimeter, with CVs of 14.9 times, 8.14 times, and 4.41 times for L*, a*, and b*, respectively. To assess the feasibility of the app for facial aesthetic monitoring, we tracked changes in pseudo-skin color on the cheek of a participant using skin-colored stickers. As a result, we observed the smallest color difference ∆Eab of 1.901, which can be considered as the experimentally validated detection limit using images acquired by the app. CONCLUSION: While the current monitoring method is a relative quantification approach, it contributes to evidence-based evaluations of skincare treatments.

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.

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.

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.

A faster CO2 fractional scanner system mode for skin rejuvenation. A clinical study.

BACKGROUND: The market requires ever-faster techniques, in particular for pre-rejuvenation condition. AIM: The purpose of this study was to assess if a fractional CO2 scanner modality (called moveo) results in a faster full-face rejuvenation treatment in comparison to the standard mode, currently existing in the scanner system. MATERIALS AND METHODS: A total of 12 female patients affected by fine lines participated in a split-face clinical investigation and underwent to two sessions with a fractional CO2 laser system equipped with an existing and a faster dedicated scanner units. Pain was assessed using VAS. Three-dimensional clinical photographs were captured before, immediately after, 3 days, 14 days after the first treatment and immediately after the second treatment and 1 months after the last one. The uniformity and aesthetic coverage of treatments were assessed using dermatoscopy. Global aesthetic improvement scale (GAIS) was used. The time taken to treat the two sides of the face and all possible side effects were monitored. RESULTS: Following only two treatment session with both scanner modes, the patient's skin texture improved significantly, with fine lines reduction. There is no statistically significant difference in perceived pain between patients. The GAIS score showed satisfactory results following both modalities. The time parameters indicated that with the faster scanner mode the full-face treatment time was reduced by 30% compared to the standard one. No adverse effects were observed. CONCLUSIONS: The moveo modality provide faster treatment and a better final dermal aesthetic outcome than the standard procedure while maintaining the same safety profile.

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.

Skin Tone Analysis Through Skin Tone Map Generation With Optical Approach and Deep Learning.

BACKGROUND: Skin tone assessment is critical in both cosmetic and medical fields, yet traditional methods like the individual typology angle (ITA) have limitations, such as sensitivity to illuminants and insensitivity to skin redness. METHODS: This study introduces an automated image-based method for skin tone mapping by applying optical approaches and deep learning. The method generates skin tone maps by leveraging the illuminant spectrum, segments the skin region from face images, and identifies the corresponding skin tone on the map. The method was evaluated by generating skin tone maps under three standard illuminants (D45, D65, and D85) and comparing the results with those obtained using ITA on skin tone simulation images. RESULTS: The results showed that skin tone maps generated under the same lighting conditions as the image acquisition (D65) provided the highest accuracy, with a color difference of around 6, which is more than twice as small as those observed under other illuminants. The mapping positions also demonstrated a clear correlation with pigment levels. Compared to ITA, the proposed approach was particularly effective in distinguishing skin tones related to redness. CONCLUSION: Despite the need to measure the illuminant spectrum and for further physiological validation, the proposed approach shows potential for enhancing skin tone assessment. Its ability to mitigate the effects of illuminants and distinguish between the two dominant pigments offers promising applications in both cosmetic and medical diagnostics.

Intracorporeal evaluation of hyaluronic acid fillers with varied rheological properties and correlations with aesthetic outcomes.

BACKGROUND: Understanding the differences in soft tissue filler rheology and how these properties can impact clinical results is a fundamental concepts for any injector. This study aimed to assess the tissue integration characteristics of hyaluronic acid (HA) fillers manufactured with different technologies (Non-Animal Stabilized HA [HA-N] or Optimal Balance Technology [HA-O]) using ultra-high-frequency ultrasound. METHODS: Twelve female participants with mild-to-moderate midface volume loss and temporal hollowing were enrolled and treated with HA-N and/or HA-O. Participants were seen at five visits (screening/baseline [treatment], and Weeks 1 [optional touch-up], 4, 6, and 8 [follow-up visits]). Ultrasound was used to evaluate the degree of product integration. RESULTS: On ultrasound, HA-N presented with distinct borders, minimal tissue integration, and a capacity to displace tissues. Conversely, HA-O tended to spread horizontally within the same tissue plane and integrated within tissues. The volumizing capacity of the HA-O fillers was dependent on particle size. CONCLUSION: HA-N is suited for deep injections in areas such as the upper lateral cheek and under the muscle of the temporal region when a lifting effect is desired; HA-O is best suited for subcutaneous injections, in areas of dynamic movement or for patients with thin skin; and can be injected subcutaneously or supraperiosteally when a volumizing effect is desired.

LC-OCT as a tool to visualize in vivo location of dermal fillers.

BACKGROUND: Dermal fillers have emerged as a popular non-surgical solution for facial rejuvenation and enhancement. Apart from botulinum toxin injections, they are the most common non-surgical procedure performed in the US. Line-field optical coherence tomography (LC-OCT; deepLive system Damae Medical, France) represents one of the most recent developments in non-invasive skin imaging technologies. MATERIALS AND METHODS: We performed LC-OCT image acquisition on six patients that were treated with hyaluronic acid (HA) dermal fillers in various locations on the face. The images were acquired before the application of the fillers (T0), immediately after (T1), and at a 6- to 8-week (T2) follow-up visit. RESULTS: At T0, we were able to appreciate a normal-appearing epidermis, dermoepithelial junction, and dermis. At T1, the intradermal filler deposits appeared as homogeneously hyporeflective areas, clearly discernible from surrounding vessels and other structures. At T2, the deposits were distinguishable as hyporeflective areas, although they were diminished in size compared to T1. On enface view, collagen fibers had increased thickness and were more homogeneously organized and hyperreflective. CONCLUSIONS: We established the usefulness of LC-OCT in the non-invasive evaluation of dermal HA fillers to visualize both short-term and medium-term effects. LC-OCT may be a valuable tool in evaluating the precise location of filler placement and follow-up of resulting in vivo changes.

Comparison of facial skin ageing in healthy Asian and Caucasian females quantified by in vivo line-field confocal optical coherence tomography 3D imaging.

BACKGROUND: Quantitative biomarkers of facial skin aging were investigated in 109 healthy Asian female volunteers, aged 20 to 70 years. MATERIALS AND METHODS: In vivo 3D Line-field Confocal Optical Coherence Tomography (LC-OCT) imaging, enhanced by Artificial Intelligence (AI)-based quantification algorithms, was utilized to compute various metrics, including stratum corneum thickness (SC), viable epidermal (VE) thickness, and Dermal-Epidermal Junction (DEJ) undulation along with cellular metrics for the temple, cheekbone, and mandible. RESULTS: Comparison with data from a cohort of healthy Caucasian volunteers revealed similarities in the variations of stratum corneum and viable epidermis layers, as well as cellular shape and size with age in both ethnic groups. However, specific findings emerged, such as larger, more heterogeneous nuclei in both layers, demonstrated by an increase in nuclei volume and their standard deviation, and increased network atypia, all showing significant age-related variations. Caucasian females exhibited a flatter and more homogeneous epidermis, evidenced by a decreased standard deviation of the number of layers, and a less dense cellular network with fewer cells per layer, indicated by a decrease in cell surface density. CONCLUSION: Ethnicity-wise comparisons highlighted distinct biological features specific to each population. Asian individuals showed significantly higher DEJ undulation, higher compactness, and lower cell network atypia compared to their Caucasian counterparts across age groups. Differences in stratum corneum and viable epidermal thickness on the cheekbone were also significant. LC-OCT 3D imaging provides valuable insights into the aging process in different populations and underscores inherent biological differences between Caucasian and Asian female volunteers.

Post-Treatment Displacement of Facial Soft Tissue Fillers-A Retrospective Ultrasound-based Investigation of 382 Zygomatic Regions.

BACKGROUND: Clinical and ultrasound experience has revealed that after soft tissue injections of the lateral cheek, the filler may displace from the zygoma to the caudal temporal area. OBJECTIVE: To obtain more data to provide insight into product distribution when soft tissue fillers are injected in the zygomatic region. METHODS: Two hundred patients were examined with facial ultrasound imaging of the zygomatic and temporal region. Inclusion criteria were simply a positive response on the screening questionnaire as to whether or not they had filler injections placed in their lateral cheek. Control injections were also performed to the zygomatic regions of a body donor and in 10 patients ultrasound-guided. RESULTS: A correlation was found between the layers in which filler was detected on the zygoma and where it was ultimately found in the temples. Four different redistribution patterns were observed: (1) migration of filler within the superficial muscular aponeurotic system (SMAS) on the zygoma into the superficial temporal fascia. Migration of filler from the lateral suborbicularis oculi fat to (2) the deep interfacial plane of the temple or (3) to the superficial temporal fat pad; (4) migration from the supraperiosteal layer of the zygoma to the superficial temporal fat pad. Body donor and patients: filler deposits injected on the zygoma were witnessed to shift during injection into the caudal part of the temple. CONCLUSION: Soft tissue filler aliquots may be redistributed into the temples after injections of the lateral side of the zygomatic arch. The displacement follows a distinct pattern depending on the initial layer of injection.

Evaluation of Facial Aging in Different Age and Gender Groups With Computed Tomography-Based Calvarium and Face Measurements.

BACKGROUND: The human face is a complex area formed by the combination of many different components and varies among individuals according to gender, age, and ethnicity. OBJECTIVE: The aim of this study was to evaluate age-related changes in the facial and calvarial bones in a large sample of both genders. METHODS: The retrospective study included nontraumatic brain computed tomography scans of 280 Turkish adults. Participants were divided into 7-decade groups with the age ranging from 20 to 89 years. Measurements of the face and calvaria were recorded, and calvarial volume was calculated. The relationship of these parameters with age and gender was examined. RESULTS: Statistically significant differences were observed in all the facial and calvarial measurements, except the transverse diameter of the head, with increasing age in both genders. Regardless of age, no significant differences were found in facial and calvarial measurements between genders. In addition, there was a significant decrease in the calvarial volume in both genders after the seventh decade of life. CONCLUSION: The structure of the face and calvarium continues to change and differentiate throughout life. Taking these changes into account during surgical and facial rejuvenation procedures can help predict outcomes and avoid the use of incorrect techniques.

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.

Ultrasound Patterns of Vitiligo at High Frequency and Ultra-High Frequency.

OBJECTIVES: To detect ultrasonographic anatomical alterations in all the skin layers in patients with vitiligo. METHODS: A prospective observational color Doppler ultrasound study was performed in nonsegmental face and/or neck vitiligo patients without a history of previous treatments. Two sites, a lesional area and a contralateral clinically healthy region, were ultrasonographically studied and compared in the same patient. All cases were studied in high-frequency (24 MHz) and ultra-high-frequency (70 MHz) ultrasound devices with the highest axial spatial resolution available in the market. Demographic data of the sample, ultrasound grayscale, and color Doppler features were recorded and analyzed. RESULTS: Ten patients met the study criteria (60% females; mean age 49 years). All cases presented ultrasonographic undulation of the epidermis in the affected zones vs 50% in the healthy control regions, being more prominent in the vitiligo areas. Eighty percent demonstrated intense hypoechoic thin plaques in the upper dermis (subepidermal). All vitiligo areas presented thickening and hypoechogenicity of the regional hair follicles and/or pilosebaceous units. Ninety percent showed prominent sebaceous glands, and 20% demonstrated a hypoechoic cap surrounding the sebaceous glands in the lesional areas. Dermal hypervascularity was detected in 100% of the affected regions and 40% of the clinically healthy areas. CONCLUSION: Ultrasound can identify subclinical inflammatory cutaneous patterns in the epidermis, dermis, hair follicles, pilosebaceous units, and sebaceous glands in vitiligo. This noninvasive information can support early detection, monitoring, and research, including the clinical trials of drugs used to manage this devastating disease.