Hydradermabrasion through the lens of Line-Field Confocal Optical Coherence Tomography.

BACKGROUND: Hydradermabrasion, also known as "HydraFacial," is an exfoliative cosmetic procedure for skin rejuvenation that has gained popularity. Despite its increasing popularity, clinical studies validating its efficacy with non-invasive assessment of histological changes to the skin, are scarce. In this study, we used Line-Field Confocal Optical Coherence Tomography (LC-OCT), an optical imaging device, to non-invasively visualize microscopic changes to skin anatomy after hydradermabrasion treatment. MATERIALS/METHODS: Eight volunteers (Fitzpatrick skin types II-V) were recruited for this study. Images, using LC-OCT (DeepLive, DAMAE medical) were obtained before and after hydradermabrasion and at 2 weeks post-treatment. A commercially available hydradermabrasion device was utilized to perform the dermabrasion. RESULTS: In the epidermis, initially, a decrease in the average thickness of the stratum corneum, from 9.42 to 6.67 µm was visualized in LC-OCT images after hydradermabrasion. However, at 2 weeks of follow-up, the average stratum corneum thickness was 9.75 µm, resulting in an overall increase in the average thickness after treatment. Improved homogenization of the stratum corneum and decreased number of undulations in the epidermis post-treatment were also visualized. In all the subjects, the superficial dermis appeared stretched, which returned to baseline by the 2-week follow-up. At the 2-week follow-up, there were no visible differences in the quality and quantity of collagen fibers in the dermis. CONCLUSION: In our study, LC-OCT images of the epidermis and dermis demonstrated microscopic features of skin rejuvenation when treated with hydradermabrasion. Thus, not only highlighting the efficacy of hydradermabrasion but also the potential of LC-OCT to serve as a tool for visualizing the microscopic effects of cosmetic procedures on skin anatomy.

A Protocol for the Automated Assessment of Cutaneous Pathology in a Mouse Model of Hemichannel Dysfunction.

In this chapter, we provide detailed instructions to perform quantitative reflectance imaging in a mouse model of a rare epidermal disorder caused by hyperactive connexin 26 hemichannels. Reflectance imaging is a versatile and powerful tool in dermatology, offering noninvasive, high-resolution insights into skin pathology, which is essential for both clinical practice and research. This approach offers several advantages and applications. Unlike traditional biopsy, reflectance imaging is noninvasive, allowing for real-time, in vivo examination of the skin. This is particularly valuable for monitoring chronic conditions or assessing the efficacy of treatments over time, enabling the detailed examination of skin morphology. This is crucial for identifying features of skin diseases such as cancers, inflammatory conditions, and infections. In therapeutic applications, reflectance imaging can be used to monitor the response of skin lesions to treatments. It can help in identifying the most representative area of a lesion for biopsy, thereby increasing the diagnostic accuracy. Reflectance imaging can also be used to diagnose and monitor inflammatory skin diseases, like psoriasis and eczema, by visualizing changes in skin structure and cellular infiltration. As the technology becomes more accessible, it has potential in telemedicine, allowing for remote diagnosis and monitoring of skin conditions. In academic settings, reflectance imaging can be a powerful research tool, enabling the study of skin pathology and the effects of novel treatments, including the development of monoclonal antibodies for therapeutic applications.

Mapping cutaneous field carcinogenesis of nonmelanoma skin cancer using mesoscopic imaging of pro-inflammation cues.

Nonmelanoma skin cancers remain the most widely diagnosed types of cancers globally. Thus, for optimal patient management, it has become imperative that we focus our efforts on the detection and monitoring of cutaneous field carcinogenesis. The concept of field cancerization (or field carcinogenesis), introduced by Slaughter in 1953 in the context of oral cancer, suggests that invasive cancer may emerge from a molecularly and genetically altered field affecting a substantial area of underlying tissue including the skin. A carcinogenic field alteration, present in precancerous tissue over a relatively large area, is not easily detected by routine visualization. Conventional dermoscopy and microscopy imaging are often limited in assessing the entire carcinogenic landscape. Recent efforts have suggested the use of noninvasive mesoscopic (between microscopic and macroscopic) optical imaging methods that can detect chronic inflammatory features to identify pre-cancerous and cancerous angiogenic changes in tissue microenvironments. This concise review covers major types of mesoscopic optical imaging modalities capable of assessing pro-inflammatory cues by quantifying blood haemoglobin parameters and hemodynamics. Importantly, these imaging modalities demonstrate the ability to detect angiogenesis and inflammation associated with actinically damaged skin. Representative experimental preclinical and human clinical studies using these imaging methods provide biological and clinical relevance to cutaneous field carcinogenesis in altered tissue microenvironments in the apparently normal epidermis and dermis. Overall, mesoscopic optical imaging modalities assessing chronic inflammatory hyperemia can enhance the understanding of cutaneous field carcinogenesis, offer a window of intervention and monitoring for actinic keratoses and nonmelanoma skin cancers and maximise currently available treatment options.

An evaluation of the effectiveness of 'ULTRACOL 200' in enhancing nasolabial fold wrinkles through cutaneous repair.

BACKGROUND: Injectable filler, a nonsurgical beauty method, has gained popularity in rejuvenating sagging skin. In this study, polydioxanone (PDO) was utilized as the main component of the ULTRACOL200 filler that helps stimulate collagenesis and provide skin radiant effects. The study aimed to evaluate and compare the effectiveness of ULTRACOL200 with other commercialized products in visually improving dermatological problems. METHODS: Herein, 31 participants aged between 20 and 59 years were enrolled in the study. 1 mL of the testing product, as well as the quantity for the compared groups was injected into each participants face side individually. Subsequently, skin texture and sunken volume of skin were measured using ANTERA 3D CS imaging technology at three periods: before the application, 4 weeks after the initial application, and 4 weeks after the 2nd application of ULTRACOL200. RESULTS: The final results of skin texture and wrinkle volume evaluation consistently demonstrated significant enhancement. Consequently, subjective questionnaires were provided to the participants to evaluate the efficacy of the testing product, illustrating satisfactory responses after the twice applications. CONCLUSION: The investigation has contributed substantially to the comprehension of a PDO-based filler (ULTRACOL200) for skin enhancement and provided profound insight for future clinical trials.

Parameter-based transfer learning for severity classification of atopic dermatitis using hyperspectral imaging.

BACKGROUND/PURPOSE: Because atopic dermatitis (AD) is a chronic inflammatory skin condition that causes structural changes, there is a growing need for noninvasive research methods to evaluate this condition. Hyperspectral imaging (HSI) captures skin structure features by exploiting light wavelength variations in penetration depth. In this study, parameter-based transfer learning was deployed to classify the severity of AD using HSI. Therefore, we aimed to obtain an optimal combination of classification results from the four models after constructing different source- and target-domain datasets. METHODS: We designated psoriasis, skin cancer, eczema, and AD datasets as the source datasets, and the set of images acquired via hyperspectral camera as the target dataset for wavelength-specific AD classification. We compared the severity classification performances of 96 combinations of sources, models, and targets. RESULTS: The highest classification performance of 83% was achieved when ResNet50 was trained on the augmented psoriasis dataset as the source, with the resulting parameters used to train the model on the target Near-infrared radiation (NIR) dataset. The second highest classification accuracy of 81% was achieved when ResNet50 was trained on the unaugmented psoriasis dataset as the source, with the resulting parameters used to train the model on the target R dataset. ResNet50 demonstrated potential as a generalized model for both the source and target data, also confirming that the psoriasis dataset is an effective training resource. CONCLUSION: The present study not only demonstrates the feasibility of the severity classification of AD based on hyperspectral images, but also showcases combinations and research scalability for domain exploration.

A novel SpaSA based hyper-parameter optimized FCEDN with adaptive CNN classification for skin cancer detection.

Skin cancer is the most prevalent kind of cancer in people. It is estimated that more than 1 million people get skin cancer every year in the world. The effectiveness of the disease's therapy is significantly impacted by early identification of this illness. Preprocessing is the initial detecting stage in enhancing the quality of skin images by removing undesired background noise and objects. This study aims is to compile preprocessing techniques for skin cancer imaging that are currently accessible. Researchers looking into automated skin cancer diagnosis might use this article as an excellent place to start. The fully convolutional encoder-decoder network and Sparrow search algorithm (FCEDN-SpaSA) are proposed in this study for the segmentation of dermoscopic images. The individual wolf method and the ensemble ghosting technique are integrated to generate a neighbour-based search strategy in SpaSA for stressing the correct balance between navigation and exploitation. The classification procedure is accomplished by using an adaptive CNN technique to discriminate between normal skin and malignant skin lesions suggestive of disease. Our method provides classification accuracies comparable to commonly used incremental learning techniques while using less energy, storage space, memory access, and training time (only network updates with new training samples, no network sharing). In a simulation, the segmentation performance of the proposed technique on the ISBI 2017, ISIC 2018, and PH2 datasets reached accuracies of 95.28%, 95.89%, 92.70%, and 98.78%, respectively, on the same dataset and assessed the classification performance. It is accurate 91.67% of the time. The efficiency of the suggested strategy is demonstrated through comparisons with cutting-edge methodologies.

Correlation analysis between texture features and elasticity of skin hyperspectral images in the near-infrared band.

BACKGROUND/PURPOSE: Skin elasticity was used to evaluate healthy and diseased skin. Correlation analysis between image texture characteristics and skin elasticity was performed to study the feasibility of assessing skin elasticity using a non-contact method. MATERIALS AND METHODS: Skin images in the near-infrared band were acquired using a hyperspectral camera, and skin elasticity was obtained using a skin elastimeter. Texture features of the mean, standard deviation, entropy, contrast, correlation, homogeneity, and energy were extracted from the acquired skin images, and a correlation analysis with skin elasticity was performed. RESULTS: The texture features, and skin elasticity of skin images in the near-infrared band had the highest correlation on the side of eye and under of arm, and the mean and correlation were features of texture suitable for distinguishing skin elasticity according to the body part. CONCLUSION: In this study, we performed elasticity and correlation analyses for various body parts using the texture characteristics of skin hyperspectral images in the near-infrared band, confirming a significant correlation in some body parts. It is expected that this will be used as a cornerstone of skin elasticity evaluation research using non-contact methods.

Characterization of the collagen network of human cheek skin using ultrasonic microscopy.

Dermal collagen is the most abundant component of human skin and has a network structure that regulates the mechanical properties of the skin. Therefore, non-invasive characterization of the collagen network would be beneficial for the evaluation of skin conditions. The microscopic substructures of the network, which are individual bundles and fibers, have been optically investigated. However, the macroscopic structure of the collagen network has not been assessed. To evaluate the dermal collagen network, we developed two new indicators, volume filling factor (VFF) and collagen fiber texture (CFT), to analyze three-dimensional echo intensity maps of high-frequency ultrasonic microscopy. By identifying the difference in the elastic modulus components of the dermal layer of facial skin, the density and texture of the collagen network were characterized using VFF and CFT, respectively. These new indicators revealed that the density decreased and the texture became fine with facial age. This study demonstrates that ultrasonic microscopy is useful for investigating skin conditions, paving the way for diagnostic applications in dermatology and aesthetic medicine.

HDS-Net: Achieving fine-grained skin lesion segmentation using hybrid encoding and dynamic sparse attention.

Skin cancer is one of the most common malignant tumors worldwide, and early detection is crucial for improving its cure rate. In the field of medical imaging, accurate segmentation of lesion areas within skin images is essential for precise diagnosis and effective treatment. Due to the capacity of deep learning models to conduct adaptive feature learning through end-to-end training, they have been widely applied in medical image segmentation tasks. However, challenges such as boundary ambiguity between normal skin and lesion areas, significant variations in the size and shape of lesion areas, and different types of lesions in different samples pose significant obstacles to skin lesion segmentation. Therefore, this study introduces a novel network model called HDS-Net (Hybrid Dynamic Sparse Network), aiming to address the challenges of boundary ambiguity and variations in lesion areas in skin image segmentation. Specifically, the proposed hybrid encoder can effectively extract local feature information and integrate it with global features. Additionally, a dynamic sparse attention mechanism is introduced, mitigating the impact of irrelevant redundancies on segmentation performance by precisely controlling the sparsity ratio. Experimental results on multiple public datasets demonstrate a significant improvement in Dice coefficients, reaching 0.914, 0.857, and 0.898, respectively.

Variations of skin thermal diffusivity on different skin regions.

BACKGROUND AND OBJECTIVE: Skin thermal diffusivity plays a crucial role in various applications, including laser therapy and cryogenic skin cooling.This study investigates the correlation between skin thermal diffusivity and two important skin parameters, melanin content and erythema, in a cohort of 102 participants. METHODS: An in-house developed device based on transient temperature measurement was used to assess thermal diffusivity at different body locations. Melanin content and erythema were measured using a colorimeter. Statistical analysis was performed to examine potential correlations. RESULTS: The results showed that the measured thermal diffusivity values were consistent with previous reports, with variations observed among subjects. No significant correlation was found between thermal diffusivity and melanin content or erythema. This suggests that other factors, such as skin hydration or epidermis thickness, may have a more dominant influence on skin thermal properties. CONLCUSION: This research provides valuable insights into the complex interplay between skin thermal properties and physiological parameters, with potential implications for cosmetic and clinical dermatology applications.

The impact of skin color and tone on histamine iontophoresis and Doppler flowmetry measurements as a pharmacodynamic biomarker.

The phenotypical manifestations of asthma among children are diverse and exhibit varying responses to therapeutic interventions. There is a need to develop objective biomarkers to improve the characterization of allergic and inflammatory responses relevant to asthma to predict therapeutic treatment responses. We have previously investigated histamine iontophoresis with laser Doppler flowmetry (HILD) as a potential surrogate biomarker that characterizes histamine response and may be utilized to guide the treatment of allergic and inflammatory disease. We have identified intra-individual variability of HILD response type among children and adults with asthma and that HILD response type varied in association with racial classification. As laser Doppler flowimetry may be impacted by skin color, we aimed to further validate the HILD method by determining if skin color or tone is associated with observed HILD response type differences. We conducted an observational study utilizing quantification of skin color and tone obtained from photographs of the skin among participants during HILD assessments via the RGB color model. We compared RGB values across racial, ethnic, and HILD response type via the Kruskal-Wallis test and calculated Kendall rank correlation coefficient to evaluate the relationship between RGB composite scores and HILD pharmacodynamic measures. We observed that RGB scores differed among racial groups and histamine response phenotypes (p < 0.05). However, there was a lack of correlation between the RGB composite score and HILD pharmacodynamic measures (r values 0.1, p > 0.05). These findings suggest that skin color may not impact HILD response variations, necessitating further research to understand previously observed differences across identified racial groups.

Microneedles: A novel clinical technology for evaluating skin characteristics.

BACKGROUND: Current methods for evaluating efficacy of cosmetics have limitations because they cannot accurately measure changes in the dermis. Skin sampling using microneedles allows identification of skin-type biomarkers, monitoring treatment for skin inflammatory diseases, and evaluating efficacy of anti-aging and anti-pigmentation products. MATERIALS AND METHODS: Two studies were conducted: First, 20 participants received anti-aging treatment; second, 20 participants received anti-pigmentation treatment. Non-invasive devices measured skin aging (using high-resolution 3D-imaging in the anti-aging study) or pigmentation (using spectrophotometry in the anti-pigmentation study) at weeks 0 and 4, and adverse skin reactions were monitored. Skin samples were collected with biocompatible microneedle patches. Changes in expression of biomarkers for skin aging and pigmentation were analyzed using qRT-PCR. RESULTS: No adverse events were reported. In the anti-aging study, after 4 weeks, skin roughness significantly improved in 17 out of 20 participants. qRT-PCR showed significantly increased expression of skin-aging related biomarkers: PINK1 in 16/20 participants, COL1A1 in 17/20 participants, and MSN in 16/20 participants. In the anti-pigmentation study, after 4 weeks, skin lightness significantly improved in 16/20 participants. qRT-PCR showed significantly increased expression of skin-pigmentation-related biomarkers: SOD1 in 15/20 participants and Vitamin D Receptor (VDR) in 15/20 participants. No significant change in TFAP2A was observed. CONCLUSION: Skin sampling and mRNA analysis for biomarkers provides a novel, objective, quantitative method for measuring changes in the dermis and evaluating the efficacy of cosmetics. This approach complements existing evaluation methods and has potential application in assessing the effectiveness of medical devices, medications, cosmeceuticals, healthy foods, and beauty devices.

Skin dark spot mapping and evaluation of brightening product efficacy using Line-field Confocal Optical Coherence Tomography (LC-OCT).

BACKGROUND: Facial dark spots remain a significant challenge for the cosmetic industry, in terms of providing effective treatment. Using Line-field Confocal Optical Coherence Tomography (LC-OCT), we investigated the internal structural features of photo-aging spot areas and evaluated the efficacy of a skin-brightening cosmetic product. MATERIALS AND METHODS: Twenty-six Asian female volunteers, aged between 29 and 65 years, applied a cosmetic product on their entire face twice a day for 2 months. LC-OCT was used to evaluate the dermal-epidermal junction (DEJ) undulation and the volume density of melanin in the epidermis at D0 and D56. Skin brightening and redness were also assessed by photography (SkinCam). RESULTS: Using LC-OCT technology, various microscopic dark spot morphologies, spanning from minimally deformed DEJ to complex DEJ patterns, were identified. Dark spots characterized by slight deformities in the DEJ were predominantly observed in the youngest age group, while older volunteers displayed a wavier pattern. Furthermore, a total of 44 spots were monitored to evaluate the brightening product efficacy. A statistically significant reduction in melanin volumetric density of 7.3% in the spots and 12.3% in their surrounding area was observed after 56 days of product application. In line with these results, an analysis of color parameters using SkinCam reveals a significant increase in brightening and decrease in redness in both pigmented spots and the surrounding skin following application. CONCLUSIONS: LC-OCT proves to be a valuable tool for in-depth dark spots characterization and assessment of skin brightening products, enabling various applications in the field of dermatological sciences.

Novel multispectral imaging to predict disease progression in pediatric morphea.

BACKGROUND: Morphea, or localized scleroderma, is an inflammatory, fibrosing skin disorder that can be progressive and debilitating. Infrared thermography frequently has false positive results. The aim of this study was to assess the ability of multispectral imaging to predict disease progression in children with morphea. METHODS: Children with morphea were recruited between 2016 and 2022. Multispectral images of affected and matched contralateral unaffected sites were obtained using the Antera™ 3D camera. Clinical assessment was performed using the Localized Scleroderma Assessment Tool (LoSCAT). Children were followed up every 3 months for imaging and clinical review. The main outcome measurement was correlation of hemoglobin gradient between affected and matched contralateral unaffected tissue and progression. RESULTS: Of 17 children, the average age was 12 years (range 6-18 years); most were female (76.5%) and white (94.1%). Nearly two-thirds (64.7%) had linear morphea, 35.2% had plaque morphea; 58.8% had been treated with systemic agents. The average LoSCAT score was 20.6 (range 5-73). The average hemoglobin gradient between affected and matched contralateral unaffected skin was four times higher in those who had progression (average differential 0.3, range 0.1-0.4) compared to those who did not (average differential 0.08, range 0.02-0.15). Using a cut off of a 0.18 hemoglobin gradient between affected and unaffected skin, the sensitivity of multispectral imaging for detecting progression in pediatric morphea is 90% with specificity of 100%. CONCLUSIONS: Multispectral imaging is a novel assessment tool with promising accuracy in predicting progression as an adjunct to clinical assessment in pediatric morphea. Further research should examine its performance against thermography.

Skinly: A novel handheld IoT device for validating biophysical skin characteristics.

BACKGROUND: Recent advancements in artificial intelligence have revolutionized dermatological diagnostics. These technologies, particularly machine learning (ML), including deep learning (DL), have shown accuracy equivalent or even superior to human experts in diagnosing skin conditions like melanoma. With the integration of ML, including DL, the development of at home skin analysis devices has become feasible. To this end, we introduced the Skinly system, a handheld device capable of evaluating various personal skin characteristics noninvasively. MATERIALS AND METHODS: Equipped with a moisture sensor and a multi-light-source camera, Skinly can assess age-related skin parameters and specific skin properties. Utilizing state-of-the-art DL, Skinly processed vast amounts of images efficiently. The Skinly system's efficacy was validated both in the lab and at home, comparing its results to established "gold standard" methods. RESULTS: Our findings revealed that the Skinly device can accurately measure age-associated parameters, that is, facial age, skin evenness, and wrinkles. Furthermore, Skinly produced data consistent with established devices for parameters like glossiness, skin tone, redness, and porphyrin levels. A separate study was conducted to evaluate the effects of two moisturizing formulations on skin hydration in laboratory studies with standard instrumentation and at home with Skinly. CONCLUSION: Thanks to its capability for multi-parameter measurements, the Skinly device, combined with its smartphone application, holds the potential to replace more expensive, time-consuming diagnostic tools. Collectively, the Skinly device opens new avenues in dermatological research, offering a reliable, versatile tool for comprehensive skin analysis.

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.

Investigation of stratum corneum cell morphology and content using novel machine-learning image analysis.

BACKGROUND: The morphology and content of stratum corneum (SC) cells provide information on the physiological condition of the skin. Although the morphological and biochemical properties of the SC are known, no method is available to fully access and interpret this information. This study aimed to develop a method to comprehensively decode the physiological information of the skin, based on the SC. Therefore, we established a novel image analysis technique based on artificial intelligence (AI) and multivariate analysis to predict skin conditions. MATERIALS AND METHODS: SC samples were collected from participants, imaged, and annotated. Nine biomarkers were measured in the samples using enzyme-linked immunosorbent assay. The data were then used to teach machine-learning models to recognize individual SC cell regions and estimate the levels of the nine biomarkers from the images. Skin physiological indicators (e.g., skin barrier function, facial analysis, and questionnaires) were measured or obtained from the participants. Multivariate analysis, including biomarker levels ​​and structural parameters of the SC as variables, was used to estimate these physiological indicators. RESULTS: We established two machine-learning models. The accuracy of recognition was assessed according to the average intersection over union (0.613), precision (0.953), recall (0.640), and F-value (0.766). The predicted biomarker levels significantly correlated with the measured levels. Skin physiological indicators and questionnaire answers were predicted with strong correlations and correct answer rates. CONCLUSION: Various physiological skin conditions can be predicted from images of the SC using AI models and multivariate analysis. Our method is expected to be useful for dermatological treatment optimization.

Multispectral near-infrared spectroscopy study evaluating the effect of razor design on shaving-induced erythema.

BACKGROUND: While shaving-induced erythema is a common inflammatory skin issue, there is a lack of quantitative information on how well a shaving product performs in this regard. In this study, multispectral near-infrared spectroscopy (NIRS) imaging was used to quantitatively and qualitatively measure the extent of shaving-induced erythema. The research compares a safety razor and a cartridge razor to evaluate their impact on skin irritation. MATERIALS AND METHODS: Fifty-nine healthy male volunteers without pre-existing skin conditions were enrolled. Basic demographics were recorded, and participants' faces or necks were imaged before shaving. Shaving was conducted on the right side of the face/neck with the safety razor and on the left side of the face/neck using the 3-blade cartridge razor. Images were captured immediately after shaving, at 5 and 10 min post-shaving. RESULTS: Tissue oxygen saturation (StO2) measurements demonstrated that the safety razor induced significantly less erythema than the cartridge razor. Immediately after shaving, 40.3% of skin shaved with the safety razor had erythema compared to 57.6% for the cartridge razor. At 5 min post-shaving, 36.5% of skin shaved with the safety razor had erythema, compared to 53.8% of cartridge razor. CONCLUSIONS: Multispectral NIRS revealed significant differences in shaving-induced erythema between safety and cartridge razors. Safety razors demonstrated a lower incidence of erythema, suggesting a potential advantage for individuals prone to skin irritation. This study contributes valuable insights into skin irritation and highlights the potential of multispectral NIRS in dermatology research.

Evaluating Skin Involvement in Systemic Sclerosis Using High-Frequency Ultrasound and Virtual Touch Tissue Imaging.

OBJECTIVE: This study aimed to explore the diagnostic significance of high-frequency ultrasound combined with visual touch tissue imaging quantification (VTIQ) in the diagnosis and management of systemic sclerosis (SSc). METHODS: Patients diagnosed with SSc and normal volunteers were recruited and divided into an experimental group and a control group, with 30 cases in each group, respectively. The skin thickness at six sites was assessed using high-frequency ultrasound, and the shear wave velocity (SWV) was determined using the VTIQ method. The differences in skin thickness and SWV between the experimental group and the control group were compared and a receiver operating characteristic (ROC) curve was plotted. The value of high-frequency ultrasound, VTIQ, and high-frequency ultrasound combined with VTIQ for evaluating skin involvement in SSc was determined. RESULTS: The difference in SWV sum at six sites and the thickness sum was statistically significant (all p = 0.000 < 0.05) from that of the control group, and there was a strong association between the SWV sum, thickness sum, and Rodnan skin score at the six sites in the experimental group (p = 0.000, r = 0.726; p = 0.000, r = 0.679). Based on the ROC curve, the area under the curve (AUC) for high-frequency ultrasound examination was 0.789. The AUC for VTIQ examination was 0.893, while the AUC for high-frequency ultrasound combined with VTIQ examination was 0.923. The combined examination method showed the highest AUC, indicating the best diagnostic performance. CONCLUSION: The integration of high-frequency ultrasound and VTIQ provides a quantitative approach for assessing the extent of skin involvement in SSc patients, offering valuable insights for clinical diagnosis and treatment purposes.

Understanding skin color bias in deep learning-based skin lesion segmentation.

BACKGROUND: The field of dermatological image analysis using deep neural networks includes the semantic segmentation of skin lesions, pivotal for lesion analysis, pathology inference, and diagnoses. While biases in neural network-based dermatoscopic image classification against darker skin tones due to dataset imbalance and contrast disparities are acknowledged, a comprehensive exploration of skin color bias in lesion segmentation models is lacking. It is imperative to address and understand the biases in these models. METHODS: Our study comprehensively evaluates skin tone bias within prevalent neural networks for skin lesion segmentation. Since no information about skin color exists in widely used datasets, to quantify the bias we use three distinct skin color estimation methods: Fitzpatrick skin type estimation, Individual Typology Angle estimation as well as manual grouping of images by skin color. We assess bias across common models by training a variety of U-Net-based models on three widely-used datasets with 1758 different dermoscopic and clinical images. We also evaluate commonly suggested methods to mitigate bias. RESULTS: Our findings expose a significant and large correlation between segmentation performance and skin color, revealing consistent challenges in segmenting lesions for darker skin tones across diverse datasets. Using various methods of skin color quantification, we have found significant bias in skin lesion segmentation against darker-skinned individuals when evaluated both in and out-of-sample. We also find that commonly used methods for bias mitigation do not result in any significant reduction in bias. CONCLUSIONS: Our findings suggest a pervasive bias in most published lesion segmentation methods, given our use of commonly employed neural network architectures and publicly available datasets. In light of our findings, we propose recommendations for unbiased dataset collection, labeling, and model development. This presents the first comprehensive evaluation of fairness in skin lesion segmentation.