Evaluating the Impact of Oleocanthal and Oleacein on Skin Aging: Results of a Randomized Clinical Study.

The prevalence of skin aging and the request for effective treatments have driven dermatological research towards natural solutions. This study investigates the anti-aging efficacy of two bioactive natural polyphenols, Oleocanthal and Oleacein, in a skincare formulation. A single-blind, randomized clinical trial involved 70 participants, using a comprehensive exclusion criterion to ensure participant safety and study integrity. Participants applied the Oleocanthal and Oleacein 1% serum formulation twice daily for 30 days. The efficacy was objectively assessed using the VISIA® Skin Analysis System at baseline, after 15 days, and after 30 days. Results indicated significant wrinkle reduction in most groups. For women aged 45-79 years, the mean change was -33.91% (95% CI: -46.75% to -21.07%). For men aged 20-44 years, it was -51.93% (95% CI: -76.54% to -27.33%), and for men aged 45-79 years, it was -46.56% (95% CI: -58.32% to -34.81%). For women aged 20-44 years, the change was -25.68% (95% CI: -63.91% to 12.54%), not statistically significant. These findings highlight the potential of EVOO-derived polyphenols in anti-aging skincare, particularly for older adults. This research paves the way for further exploration into natural compounds in dermatology, particularly for aging skin management.

Spectrum-based deep learning framework for dermatological pigment analysis and simulation.

BACKGROUND: Deep learning in dermatology presents promising tools for automated diagnosis but faces challenges, including labor-intensive ground truth preparation and a primary focus on visually identifiable features. Spectrum-based approaches offer professional-level information like pigment distribution maps, but encounter practical limitations such as complex system requirements. METHODS: This study introduces a spectrum-based framework for training a deep learning model to generate melanin and hemoglobin distribution maps from skin images. This approach eliminates the need for manually prepared ground truth by synthesizing output maps into skin images for regression analysis. The framework is applied to acquire spectral data, create pigment distribution maps, and simulate pigment variations. RESULTS: Our model generated reflectance spectra and spectral images that accurately reflect pigment absorption properties, outperforming spectral upsampling methods. It produced pigment distribution maps with correlation coefficients of 0.913 for melanin and 0.941 for hemoglobin compared to the VISIA system. Additionally, the model's simulated images of pigment variations exhibited a proportional correlation with adjustments made to pigment levels. These evaluations are based on pigment absorption properties, the Individual Typology Angle (ITA), and pigment indices. CONCLUSION: The model produces pigment distribution maps comparable to those from specialized clinical equipment and simulated images with numerically adjusted pigment variations. This approach demonstrates significant promise for developing professional-level diagnostic tools for future clinical applications.

Fabrication and efficacy assessment of combination of brimonidine and ivermectin for treatment of papulopustular rosacea.

BACKGROUND & AIM: Rosacea is a chronic inflammatory, multifactorial disease for which combination therapy could be an effective treatment. In this study, we evaluate the effect of the combination therapy of brimonidine 0.33% and ivermectin 1% as a single cream for the treatment of papulopustular rosacea. METHOD: A stable and appropriate formulation was prepared by adding the aqueous phase to the lipid phase while being stirred. The stability and physicochemical properties of the formulation were evaluated under accelerated conditions. Twelve patients (36-60 years) with mild to moderate papulopustular rosacea and a Demodex count of five or more were treated with the combination of brimonidine 0.33% and ivermectin 1% cream. Clinician's Erythema Assessment (CEA), Patients Self-Assessment (PSA), skin erythema (ΔE) and lightness (ΔL), and skin biophysical parameters including transepidermal water loss (TEWL), skin hydration, pH, and sebum content, as well as erythema and melanin index and ultrasound parameters, were measured before treatment and 4 and 8 weeks after. Adverse drug reactions were also recorded. RESULTS: CEA and PSA decreased significantly from 3 to 2 after 8 weeks, respectively (p-value = 0.014 for CEA and 0.010 for PSA). ΔE and ΔL, as well as skin erythema index and TEWL improved after 8 weeks of treatment (p < 0.05). Two patients withdrew from the study in the first week because of local adverse effects; one developed flushing following treatment and left the investigation after 4 weeks and another patient withdrew from the study after 4 weeks due to deciding to become pregnant. CONCLUSION: Eight-week treatment with the combination of brimonidine 0.33% and ivermectin 1% was shown to be effective for improvement of erythema and inflammatory lesions in mild to moderate papulopustular rosacea.

Deep learning-based skin care product recommendation: A focus on cosmetic ingredient analysis and facial skin conditions.

BACKGROUND: Recommendations for cosmetics are gaining popularity, but they are not being made with consideration of the analysis of cosmetic ingredients, which customers consider important when selecting cosmetics. AIMS: This article aims to propose a method for estimating the efficacy of cosmetics based on their ingredients and introduces a system that recommends personalized products for consumers, combined with AI skin analysis. METHODS: We constructed a deep neural network architecture to analyze sequentially arranged cosmetic ingredients in the product and incorporated skin analysis models to get the precise skin status of users from frontal face images. Our recommendation system makes decisions based on the results optimized for the individual. RESULTS: Our cosmetic recommendation system has shown its effectiveness through reliable evaluation metrics, and numerous examples have demonstrated its ability to make reasonable recommendations for various skin problems. CONCLUSION: The result shows that deep learning methods can be used to predict the effects of products based on their cosmetic ingredients and are available for use in personalized cosmetic recommendations.

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.

Deep learning-based pigment analysis model trained with optical approach and ground truth assistance.

This study introduces an integrated training method combining the optical approach with ground truth for skin pigment analysis. Deep learning is increasingly applied to skin pigment analysis, primarily melanin and hemoglobin. While regression analysis is a widely used training method to predict ground truth-like outputs, the input image resolution is restricted by computational resources. The optical approach-based regression method can alleviate this problem, but compromises performance. We propose a strategy to overcome the limitation of image resolution while preserving performance by incorporating ground truth within the optical approach-based learning structure. The proposed model decomposes skin images into melanin, hemoglobin, and shading maps, reconstructing them by solving the forward problem with reference to the ground truth for pigments. Evaluation against the VISIA system, a professional diagnostic equipment, yields correlation coefficients of 0.978 for melanin and 0.975 for hemoglobin. Furthermore, our model can produce pigment-modified images for applications like simulating treatment effects.

Progressive Observation of Covid-19 Vaccination Effects on Skin-Cellular Structures by Use of Intelligent Laser Speckle Classification (ILSC) Technique.

The recent pandemic showed that the current global research strategies on vaccine development in an emergency period necessitates more optimized supplementary techniques to observe instant progressive vaccines' subtle effects on human metabolisms to make better and speedy evolutionary health assessments. To fill this gap, we have followed a multi-disciplinary approach exploiting AI, laser-optics, and specific imaging methods. The proposed technique can make progressive observations on Covid-19 Astra Zeneca vaccination effects on skin cellular network by use of the well-established technique-Intelligent Laser Speckle Classification (ILSC), as Covid-19 is a skin-affecting systemic disease. The method also managed to distinguish between three different subject groups via their laser speckle skin image samplings, grouped as early-vaccinated, late-vaccinated and non-vaccinated participants. The results have proven that the ILSC technique, in association with the parametrically optimised Bayesian network, can classify hidden skin changes of vaccinated and non-vaccinated individuals up to 90% accuracy and is also capable of detecting instant progressive developments pertaining to skin cellular properties. The proposed method has also proven that the continuous Covid-19 vaccine effect on the sub-skin layers can be observable by high frequency and speedy non-invasive data collection in real-time with high reliability.

Deep learning-based optical approach for skin analysis of melanin and hemoglobin distribution.

Significance: Melanin and hemoglobin have been measured as important diagnostic indicators of facial skin conditions for aesthetic and diagnostic purposes. Commercial clinical equipment provides reliable analysis results, but it has several drawbacks: exclusive to the acquisition system, expensive, and computationally intensive. Aim: We propose an approach to alleviate those drawbacks using a deep learning model trained to solve the forward problem of light-tissue interactions. The model is structurally extensible for various light sources and cameras and maintains the input image resolution for medical applications. Approach: A facial image is divided into multiple patches and decomposed into melanin, hemoglobin, shading, and specular maps. The outputs are reconstructed into a facial image by solving the forward problem over skin areas. As learning progresses, the difference between the reconstructed image and input image is reduced, resulting in the melanin and hemoglobin maps becoming closer to their distribution of the input image. Results: The proposed approach was evaluated on 30 subjects using the professional clinical system, VISIA VAESTRO. The correlation coefficients for melanin and hemoglobin were found to be 0.932 and 0.857, respectively. Additionally, this approach was applied to simulated images with varying amounts of melanin and hemoglobin. Conclusion: The proposed approach showed high correlation with the clinical system for analyzing melanin and hemoglobin distribution, indicating its potential for accurate diagnosis. Further calibration studies using clinical equipment can enhance its diagnostic ability. The structurally extensible model makes it a promising tool for various image acquisition conditions.

Objective, measurable assessment of the elasticity of the skin around the eyes following the carboxytherapy treatment.

BACKGROUND: Carboxytherapy is one of the most popular procedures used in dermatology. The treatments are known to be minimally invasive and highly effective for various skin disorders. Therefore, the research carried out in the paper is purposeful and addresses an important area of aesthetic therapies. AIMS: Objective, apparatus-based assessment of the effect of carboxytherapy on skin elasticity around the eyes. PATIENTS/METHODS: The study included a group of 39 Caucasian individuals aged 35-55 years. Participants were subjected to a series of five carboxytherapy treatments of eye area at weekly intervals. The Cutometer measuring probe was used to assess skin elasticity. Additionally, photographic documentation was out using the Fotomedicus system. RESULTS: In this study, statistically significant increase in R2 parameter was found as the result of applied treatment. This parameter is considered to be the most important indicator of changes in skin elasticity. In 29 out of 39 subjects, the carboxytherapy treatment significantly improved skin tension and elasticity. Such therapy may also exert a beneficial effect on flexibility since in 28 participants the favorable change in the R7 parameter was visible. CONCLUSIONS: The objective analysis of the impact of carboxytherapy treatment on parameters determining skin elasticity revealed that this therapy can be used as an anti-aging method in the eye area. Obtained results are in agreement with other biochemical, histological, and photographical documentation analyses of the effect of carboxytherapy on skin, performed by independent researchers.

Comparison of two devices for facial skin analysis.

BACKGROUND: The use of quantitative measures to assess patients' skin has turned the subjective nature of dermatology into an objective one. These measures allow for more accurate diagnosis and improved monitoring of diseases. While there are many skin analysis devices available, they often require specific equipment to function and can be costly, limiting their clinical use. AIMS: The aim of our study was to compare a new skin analysis device that is easy to incorporate into dermatologic practice to a standard validated device. METHODS: We recruited 50 patients from dermatology clinics and analyzed their skin with two facial analysis systems: an online application that can be downloaded on either an iPad or iPhone, and a computer-based analysis system that utilizes a facial imaging photobooth. Scores were recorded and compared between the two devices for the following five skin characteristics; "spots," "wrinkles," "redness," "texture," and "pores." RESULTS: The tablet and the computer analysis system had an agreement rate of 67.7%. The highest agreement was in assessing texture (72.0%) and pores (68.2%). The lowest agreeance was in assessing redness (64%) and wrinkles (67%). When assessing the relationship between patients' raw score for wrinkles and their age, there was a strong correlation with the tablet (r = 0.79, p < 0.0001) compared with the computer system (r = 0.26, p < 0.06). CONCLUSIONS: The application utilized in our study may serve as a cost-effective and clinic friendly tool to assess patients' skin. It produces similar results to other skin analysis devices and may be more sensitive in detecting and quantifying wrinkles.

Initial validation of a new device for facial skin analysis.

The field of dermatology is met with many subjective analysis methods. Due to the relative nature of subjective analysis methods, objective analysis methods with greater accuracy and reliability were developed. Many of these devices are either inaccessible to patients without being a part of a clinical trial, bulky, or costly. However, with the advances in artificial intelligence and handheld devices, measurement methods have become simplified. The purpose of our study was to validate an objective skin analysis software available on a handheld device by comparing it to a board-certified dermatologist's assessment. Participants of various ages and skin types were analyzed with the facial analysis system on an iPad Pro. The same photographs were ranked by a physician based on 14 common skin characteristics. The facial analysis system and the physician's rankings had a good agreement rate of 69%. The greatest agreement rates were with the assessment of erythema (83.7%) and wrinkles (81.6%) and the lowest with oiliness (53.1%). The analysis system's high re-test reliability and good agreement rates with physician assessment support its potential use in the clinical setting.

The role of artificial intelligence in cosmetic dermatology-Current, upcoming, and future trends.

Within the field of cosmetic dermatology, several promising developments utilize artificial intelligence to better patient care. While many new treatments in cosmetic dermatology feature components of artificial intelligence, there is a knowledge gap within the field regarding the current and developing products featuring AI. We aim to highlight current and developing applications of artificial intelligence in cosmetic dermatology and provide insight into future modalities in this field. Methods include literature review, including peer-reviewed journal articles as well as product websites. In an age of medical and technological advancement, the utility of artificial intelligence models continues to grow.There are many new facets of artificial intelligence in cosmetic dermatology, marketed to both the consumer and the physician. With the development of customizable skin care, augmented reality applications, and at-home skin analysis tools, patients are empowered to be the masters of their cosmetic care. Artificial intelligence is utilized by physicians in new ways in their practices, with the advent of models for prediction of clinical outcome to treatments and tools for in-depth analysis of the patient's skin. Further research is required in the development of automated energy-based treatment devices and robotic-assisted treatments. Models for AI in cosmetic dermatology serve to increase patient involvement in their skin care decisions and have the ability to enhance the patient-physician experience. Dermatologists should be well-informed of the emerging technologies to better educate patients and enhance their clinical practice.

Clinical Assessment of New Topical Cream Containing Two Essential Oils Combined with Tretinoin in the Treatment of Acne.

BACKGROUND: Acne is a frequent adolescent disease characterized by inflammatory and non-inflammatory lesions whose topical treatment very often presents adverse phenomena such as irritation or resistance to antibiotics that reduce the patient's compliance. The purpose of this study is to compare a commercial product (Acnatac gel) based on clindamycin-tretinoin (CTG) with a galenic compound containing 2 essential oils (Myrtus communisL. and Origanum vulgare) and tretinoin (MOTC) to evaluate its anti-acne effectiveness and action on the microclimate of the skin. METHODS: Sixty volunteers were randomly divided into an A group using MOTC and a B group, as a positive control, using CTG. The effectiveness was assessed with non-invasive skin analysis (Sebumeter, pH meter, Tewameter and Mexameter) and the counts of the number of lesions, after 15 and 30 days. RESULTS: In both groups, there is a worsening of transepidermal water loss (TEWL) due to tretinoin. MOTC has improved, starting from 15 days of treatment, the papular erythema (p = 0.0329 vs CTG) and has reduced at all times even the rashes of retinoids present in the healthy perilesional skin (p = 0.0329 and p = 0.0017, respectively, at 15 and 30 days). CONCLUSION: MOTC has shown, compared to Acnatac, to have anti-acne efficacy and to possess an anti-inflammatory activity, due to essential oils, able to reduce in vivo erythematous lesions and those induced by retinoids.

3D surface texture analysis of high-resolution normal fields for facial skin condition assessment.

BACKGROUND: This paper investigates the use of a light stage to capture high-resolution, 3D facial surface textures and proposes novel methods to use the data for skin condition assessment. MATERIALS AND METHODS: We introduce new methods for analysing 3D surface texture using high-resolution normal fields and apply these to the detection and assessment of skin conditions in human faces, specifically wrinkles, pores and acne. The use of high-resolution normal maps as input to our texture measures enables us to investigate the 3D nature of texture, while retaining aspects of some well-known 2D texture measures. The main contributions are as follows: the introduction of three novel methods for extracting texture descriptors from high-resolution surface orientation fields; a comparative study of 2D and 3D skin texture analysis techniques; and an extensive data set of high-resolution 3D facial scans presenting various skin conditions, with human ratings as "ground truth." RESULTS: Our results demonstrate an improvement on state-of-the-art methods for the analysis of pores and comparable results to the state of the art for wrinkles and acne using a considerably more compact model. CONCLUSIONS: The use of high-resolution normal maps, captured by a light stage, and the methods described, represent an important new set of tools in the analysis of skin texture.

Non-invasive methods for evaluation of skin manifestations in patients with ichthyosis.

Hereditary ichthyoses include a group of diseases characterized by hyperkeratosis, scaling, generalized xerosis, and is frequently associated with erythroderma. They are classified as syndromic and non-syndromic entities. The monitoring of the severity of ichthyosis requires different strategies for immediate analysis, which can comprise visual analogue scales or non-invasive quantitative methods, which collect information on disease progression that may contribute to the management of ichthyosis and aid in delineating clinical trials. In this article, we present a comprehensive review of the existing visual analogue scales, their validation, and their use in studies of disease severity and clinical trials. Interestingly, after many years of study, to date there is not a unanimously accepted tool for assessing the harshness of clinical features. Therefore, we discuss the perspectives of some non-invasive quantitative methods and strategies employed in clinical studies performed in patients with ichthyosis. Advances in these methods provide a rationale of their potential application in the evaluation of ichthyosis severity. Our purpose is to show an overview of non-invasive methodologies for the study of the harshness of ichthyosis.

Portable Skin Analyzers with Simultaneous Measurements of Transepidermal Water Loss, Skin Conductance and Skin Hardness.

Simultaneous measurement of skin physiological and physical properties are important for the diagnosis of skin diseases and monitoring of human performance, since it provides more comprehensive understanding on the skin conditions. Current skin analysis devices, however, require each of probes and unique protocols for the measurement of individual skin properties, resulting in inconvenience and increase of measurement uncertainty. This paper presents a pen-type skin analyzing device capable tomeasure three key skin properties at the same time: transepidermal water loss (TEWL), skin conductance, and skin hardness. It uses a single truncated hollow cone (THC) probe integrated with a humidity sensor, paired electrodes, and a load cell for the multimodal assessment of the skin properties. The present device measured TEWL with a sensitivity of 0.0068 (%/s)/(g/m2/h) and a linearity of 99.63%, conductance with a sensitivity of 1.02 µS/µS and a linearity of 99.36%, and hardness with a sensitivity of 0.98 Shore 00/Shore 00 and a linearity of 99.85%, within the appropriate ranges for the human skin. The present pen-type device has a high potential for the skin health diagnosis as well as the human performance monitoring applications.

Truncated Hollow Cone Probe for Assessing Transepidermal Water Loss and Skin Hardness.

This study proposes a skin analysis device using a truncate hollow cone (THC) probe for measuring both transepidermal water loss (TEWL) and skin hardness. Because skin health is closely related to the epidermal barrier function and skin mechanical property, it is important to measure their biophysical indicators at the same time, to understand skin conditions and diagnose skin disorders such as atopic dermatitis and systemic sclerosis. Previous skin analyzers, however, required different probes with different protocols for each biophysical indicators, which makes the measurement inconvenient and increases the measurement uncertainty. The present device consists of a THC probe equipped with humidity and force sensors, and an actuator that simultaneously measure both TEWL and skin hardness which indicate the integrity of the epidermal barrier function and the skin mechanical property, respectively. Using artificial reference skins, the prototype device showed the TEWL with a sensitivity and linearity of 0.011 (%/s)/(g/m2/h) and 99.5%, and the hardness with 0.075 N/(Shore 00) and 97.6%, respectively, which are within the appropriate range for the properties of human skin. The on-body measurement of five subjects showed that the proposed device could measure both the TEWL and skin hardness without any crosstalk from each other. The proposed device has great potential for in-depth analysis of the health status of the skin which could indicate various skin diseases.

Quantifying facial skin erythema more precisely by analyzing color channels of The VISIA Red images.

BACKGROUND: The VISIA Red images were developed to document and measure facial skin erythema, but diffuse erythema cannot be fully segmented by the VISIA system due to the automatic thresholding segmentation method. Moreover, topical area analysis is not available in the system. MATERIALS AND METHODS: Erythema severity degrees of 20 simulated Red images were designated 1-20 with 1-20 inflammatory lesions for each, respectively. The RGB channel mean values of each simulated image were acquired by ImageJ and relative intensity of red values calculated. RESULTS: The relative intensity of red values positively correlate to erythema severity with a coefficient of 0.999345 (p < 0.001). We also proposed a method for calibration when pustules were present in the erythema area. The method was proved by mathematical reasoning and verified by certified dermatologists. CONCLUSION: We demonstrated a simple and more precise method to quantify and compare facial skin erythema by analyzing the RGB channel values of the VISIA Red images. Our method brings convenience for erythema evaluation in dermatological studies.