Skin erythema assessment techniques

Abdlaty, Ramy; Fang, Qiyin

doi:10.1016/j.clindermatol.2021.03.006

Cited by 14 publications

(14 citation statements)

References 117 publications

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“…Visual assessment is the gold standard for the evaluation of skin erythema. In this method, an experienced investigator measures the intensity of skin erythema by dividing the scale from 3 to 10 points without a tool 5 . Even though this method is fast and convenient, it is subjective, and accurate color communication is difficult because the result value may be different for each investigator.…”

Section: Introductionmentioning

confidence: 99%

“…The redness of the face is influenced by the distribution of blood vessels and nerves in the face. 5 Facial erythema can occur regardless of race, but the degree of erythema may look different depending on skin color. Postinflammatory hyperpigmentation may mask facial erythema.…”

mentioning

confidence: 99%

“…In this method, an experienced investigator measures the intensity of skin erythema by dividing the scale from 3 to 10 points without a tool. 5 Even though this method is fast and convenient, it is subjective, and accurate color communication is difficult because the result value may be different for each investigator. The erythema index has been studied in various ways, including the semiquantitative grading scale, the clinician's erythema assessment, and computer-based algorithms.…”

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confidence: 99%

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Analysis of facial vascular pattern characteristics in the Korean population

Kim

Lee²,

Han

et al. 2023

Skin Research and Technology

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Background: Facial erythema is a common problem among patients visiting dermatologists. However, data on the clinical characteristics of facial erythema in healthy people are lacking. We aimed to compare and analyze the severity and pattern of facial vascularity in healthy subjects based on their age and gender. Materials and Methods:This study included 198 Korean volunteers (126 females and 72 males) with Fitzpatrick skin types II, III, or IV. Fourteen different anatomical areas on the face were divided into facial erythema units. Each unit was scored from one (least erythematous) to five (most erythematous) according to the observed level of erythema on the red images implemented as hemoglobin content. We also evaluated the presence of facial telangiectatic macules. Results:On average, the perinasal, nasal, and cheek units were the most hypervascular regions. In contrast, the degree of facial erythema was lowest in the labial (perioral), neck, and temporal regions. The average value of erythema was higher in males than in females. Additionally, the severity of erythema tended to increase with age. In both males and females, the number of telangiectatic macules increased with age. Conclusions:We analyzed the clinical characteristics of erythema in healthy subjects with Fitzpatrick skin types II, III, or IV in the Korean population. This study is expected to be used to identify the neurovascular pathogenesis of the most common regions of facial dermatosis in the future.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Analysis of facial vascular pattern characteristics in the Korean population

Kim

Lee²,

Han

et al. 2023

Skin Research and Technology

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“…The epidermis is partly responsible for skin color, but the dermal fiber network and the perfusion are also very relevant in Fitzpatrick skin types 1 to 3. The reddening of the skin as a result of an external stimulation, immunologic response with or without hypersensitivity to an allergen, or a viral infection is known as skin erythema [ 11 ]. Another important parameter in skin physiology is melanin, which is found in melanosomes.…”

Section: Introductionmentioning

confidence: 99%

The Assessment of Skin Homeostasis Changes after Using Different Types of Excipients in Healthy Individuals

Ordoñez-Toro

Montero‐Vílchez

Muñoz-Baeza

et al. 2022

IJERPH

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Excipients are used as vehicles for topical treatments; however, there are not many studies that evaluate the impact of different excipients themselves. The aim of this research is to assess skin homeostasis changes in healthy individuals after using water/oil (W/O), oil/water (O/W), Beeler base, foam and Vaseline excipients. A within-person randomized trial was conducted that included healthy individuals without previous skin diseases. Skin barrier function parameters, including stratum corneum hydration (SCH), transepidermal water loss (TEWL), pH, temperature, erythema, melanin and elasticity (R0, R2, R5 and R7), were measured on the volar forearm before and after using each excipient. Sixty participants were included in the study, with a mean age of 32 years. After applying w/o excipient erythema decreased by 25 AU, (p < 0.001) and elasticity increased by 6%. After using the o/w excipient, erythema decreased by 39.36 AU (p < 0.001) and SCH increased by 6.85 AU (p = 0.009). When applying the Beeler excipient, erythema decreased by 41.23 AU (p < 0.001) and SCH increased by 15.92 AU (p < 0.001). Foam and Vaseline decreased TEWL and erythema. Excipients have a different impact on skin barrier function. Knowing the effect of excipients on the skin could help to develop new topical treatments and help specialists to choose the best excipient according to the pathology.

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“…To obtain standardized images, a large amount of expensive equipment and expertise is required. In addition, this method requires no contact with the skin and large areas can be evaluated [13]. Hyperspectral imaging combines the advantages of both methods by illuminating the skin and measuring the reflected light at a distance.…”

Section: Introductionmentioning

confidence: 99%

The mathematics of erythema: Development of machine learning models for artificial intelligence assisted measurement and severity scoring of radiation induced dermatitis

Ranjan¹,

Partl

Erhart³

et al. 2021

Preprint

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Although significant advancements in computer-aided diagnostics using artificial intelligence (AI) have been made, to date, no viable method for radiation-induced skin reaction (RISR) analysis and classification is available. The objective of this single-center study was to develop machine learning and deep learning approaches using deep convolutional neural networks (CNNs) for automatic classification of RISRs according to the Common Terminology Criteria for Adverse Events (CTCAE) grading system. ScarletredⓇ Vision, a novel and state-of-the-art digital skin imaging method capable of remote monitoring and objective assessment of acute RISRs was used to convert 2D digital skin images using the CIELAB color space and conduct SEV* measurements. A set of different machine learning and deep convolutional neural network-based algorithms has been explored for the automatic classification of RISRs. A total of 2263 distinct images from 209 patients were analyzed for training and testing the machine learning and CNN algorithms. For a 2-class problem of healthy skin (grade 0) versus erythema (grade ≥ 1), all machine learning models produced an accuracy of above 70%, and the sensitivity and specificity of erythema recognition were 67-72% and 72-83%, respectively. The CNN produced a test accuracy of 74%, sensitivity of 66%, and specificity of 83% for predicting healthy and erythema cases. For the severity grade prediction of a 3-class problem (grade 0 versus 1 versus 2), the test accuracy was 60-67%, and the sensitivity and specificity were 56-82%, 35-59%, and 65-72%, respectively. For estimating the severity grade of each class, the CNN obtained an accuracy of 73%, 66%, and 82%, respectively. Ensemble learning combines several individual predictions to obtain a better generalization performance. Furthermore, we exploited ensemble learning by deploying a CNN model as a meta-learner. The ensemble CNN based on bagging and majority voting shows an accuracy, sensitivity and specificity of 87%, 90%, and 82% for a 2-class problem, respectively. For a 3-class problem, the ensemble CNN shows an overall accuracy of 66%, while for each grade (0, 1, and 2) accuracies were 0.76%, 0.69%, and 0.87%, sensitivities were 0.70%, 0.57%, and 0.71%, and specificities were 0.78%, 0.75%, and 0.95%, respectively. This study is the first to focus on erythema in radiation-dermatitis and produces benchmark results using machine learning models. The outcome of this study validates that the proposed system can act as a pre-screening and decision support tool for oncologists or patients to provide fast, reliable, and efficient assessment of erythema grading.

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Skin erythema assessment techniques

Cited by 14 publications

References 117 publications

Analysis of facial vascular pattern characteristics in the Korean population

Analysis of facial vascular pattern characteristics in the Korean population

The Assessment of Skin Homeostasis Changes after Using Different Types of Excipients in Healthy Individuals

The mathematics of erythema: Development of machine learning models for artificial intelligence assisted measurement and severity scoring of radiation induced dermatitis

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